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f0f14b3e7269637ccb929ad354057370b4d07a38 |
# Dataset of voroshilov/ヴォロシーロフ/伏罗希洛夫 (Azur Lane)
This is the dataset of voroshilov/ヴォロシーロフ/伏罗希洛夫 (Azur Lane), containing 60 images and their tags.
The core tags of this character are `breasts, long_hair, blue_hair, large_breasts, bangs, purple_eyes, very_long_hair, hair_ornament, hair_flower`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 60 | 107.14 MiB | [Download](https://huggingface.co/datasets/CyberHarem/voroshilov_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 60 | 52.77 MiB | [Download](https://huggingface.co/datasets/CyberHarem/voroshilov_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 157 | 114.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/voroshilov_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 60 | 90.69 MiB | [Download](https://huggingface.co/datasets/CyberHarem/voroshilov_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 157 | 170.93 MiB | [Download](https://huggingface.co/datasets/CyberHarem/voroshilov_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/voroshilov_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 17 |  |  |  |  |  | 1girl, looking_at_viewer, solo, black_thighhighs, cleavage, bare_shoulders, flower, garter_straps, earrings, thighs, blush, white_dress, covered_navel, wide_sleeves, cowboy_shot, white_leotard, fur-trimmed_coat, parted_lips, simple_background, white_background, open_coat |
| 1 | 23 |  |  |  |  |  | 1girl, solo, looking_at_viewer, blush, cleavage, collarbone, wet, naked_towel, thighs, bare_shoulders, sitting, closed_mouth, onsen, water, parted_lips, red_eyes |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | black_thighhighs | cleavage | bare_shoulders | flower | garter_straps | earrings | thighs | blush | white_dress | covered_navel | wide_sleeves | cowboy_shot | white_leotard | fur-trimmed_coat | parted_lips | simple_background | white_background | open_coat | collarbone | wet | naked_towel | sitting | closed_mouth | onsen | water | red_eyes |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:-------------------|:-----------|:-----------------|:---------|:----------------|:-----------|:---------|:--------|:--------------|:----------------|:---------------|:--------------|:----------------|:-------------------|:--------------|:--------------------|:-------------------|:------------|:-------------|:------|:--------------|:----------|:---------------|:--------|:--------|:-----------|
| 0 | 17 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | |
| 1 | 23 |  |  |  |  |  | X | X | X | | X | X | | | | X | X | | | | | | | X | | | | X | X | X | X | X | X | X | X |
| CyberHarem/voroshilov_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T17:46:13+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T18:00:58+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of voroshilov/ヴォロシーロフ/伏罗希洛夫 (Azur Lane)
===============================================
This is the dataset of voroshilov/ヴォロシーロフ/伏罗希洛夫 (Azur Lane), containing 60 images and their tags.
The core tags of this character are 'breasts, long\_hair, blue\_hair, large\_breasts, bangs, purple\_eyes, very\_long\_hair, hair\_ornament, hair\_flower', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
e976c2a0c3ab3e4308bbcb8ea70b38703d451112 |
# Dataset of hermann_kunne/ヘルマン・キュンネ/Z19 (Azur Lane)
This is the dataset of hermann_kunne/ヘルマン・キュンネ/Z19 (Azur Lane), containing 26 images and their tags.
The core tags of this character are `black_hair, long_hair, hat, bangs`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 26 | 33.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hermann_kunne_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 26 | 18.94 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hermann_kunne_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 68 | 42.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hermann_kunne_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 26 | 28.51 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hermann_kunne_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 68 | 60.92 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hermann_kunne_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/hermann_kunne_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 6 |  |  |  |  |  | 1girl, blush, grey_eyes, looking_at_viewer, midriff, navel, solo, black_jacket, black_skirt, blunt_bangs, pleated_skirt, simple_background, very_long_hair, belt, black_headwear, crop_top, garter_straps, long_sleeves, open_jacket, peaked_cap, red_bowtie, smile, thighhighs, white_background, grey_shirt, open_mouth, outstretched_arms, suspenders, v-shaped_eyebrows |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | grey_eyes | looking_at_viewer | midriff | navel | solo | black_jacket | black_skirt | blunt_bangs | pleated_skirt | simple_background | very_long_hair | belt | black_headwear | crop_top | garter_straps | long_sleeves | open_jacket | peaked_cap | red_bowtie | smile | thighhighs | white_background | grey_shirt | open_mouth | outstretched_arms | suspenders | v-shaped_eyebrows |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:------------|:--------------------|:----------|:--------|:-------|:---------------|:--------------|:--------------|:----------------|:--------------------|:-----------------|:-------|:-----------------|:-----------|:----------------|:---------------|:--------------|:-------------|:-------------|:--------|:-------------|:-------------------|:-------------|:-------------|:--------------------|:-------------|:--------------------|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/hermann_kunne_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T17:46:19+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T17:53:34+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of hermann\_kunne/ヘルマン・キュンネ/Z19 (Azur Lane)
===================================================
This is the dataset of hermann\_kunne/ヘルマン・キュンネ/Z19 (Azur Lane), containing 26 images and their tags.
The core tags of this character are 'black\_hair, long\_hair, hat, bangs', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
1b443737fb9a454586f5636a8b5d489a2c70365f |
# Dataset of isokaze/磯風/矶风 (Azur Lane)
This is the dataset of isokaze/磯風/矶风 (Azur Lane), containing 39 images and their tags.
The core tags of this character are `animal_ears, green_hair, animal_ear_fluff, hair_ornament, long_hair, green_eyes, fang, thick_eyebrows, bangs, tail, hair_between_eyes, hairband, black_hairband, very_long_hair, fox_ears`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 39 | 46.54 MiB | [Download](https://huggingface.co/datasets/CyberHarem/isokaze_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 39 | 27.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/isokaze_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 88 | 58.08 MiB | [Download](https://huggingface.co/datasets/CyberHarem/isokaze_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 39 | 41.55 MiB | [Download](https://huggingface.co/datasets/CyberHarem/isokaze_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 88 | 81.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/isokaze_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/isokaze_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 5 |  |  |  |  |  | 1girl, :d, fur_trim, long_sleeves, looking_at_viewer, navel, open_clothes, open_mouth, solo, white_thighhighs, wide_sleeves, blush, claw_pose, hair_bell, jingle_bell, full_body, groin, hands_up, platform_footwear, short_eyebrows, standing, white_skirt, zouri, ass_visible_through_thighs, flat_chest, fox_tail, magatama_necklace, midriff, pleated_skirt, red_footwear, revealing_clothes, shide, sparkle, white_background |
| 1 | 11 |  |  |  |  |  | 1girl, hair_bell, jingle_bell, solo, wide_sleeves, blush, looking_at_viewer, open_mouth, black_thighhighs, long_sleeves, :d, white_dress, white_background, standing, cat_ear_legwear, folding_fan, hair_ribbon, holding_fan, bandages, black_capelet, cat_ears, full_body, paw_print, simple_background, tabi, tassel |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | :d | fur_trim | long_sleeves | looking_at_viewer | navel | open_clothes | open_mouth | solo | white_thighhighs | wide_sleeves | blush | claw_pose | hair_bell | jingle_bell | full_body | groin | hands_up | platform_footwear | short_eyebrows | standing | white_skirt | zouri | ass_visible_through_thighs | flat_chest | fox_tail | magatama_necklace | midriff | pleated_skirt | red_footwear | revealing_clothes | shide | sparkle | white_background | black_thighhighs | white_dress | cat_ear_legwear | folding_fan | hair_ribbon | holding_fan | bandages | black_capelet | cat_ears | paw_print | simple_background | tabi | tassel |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----|:-----------|:---------------|:--------------------|:--------|:---------------|:-------------|:-------|:-------------------|:---------------|:--------|:------------|:------------|:--------------|:------------|:--------|:-----------|:--------------------|:-----------------|:-----------|:--------------|:--------|:-----------------------------|:-------------|:-----------|:--------------------|:----------|:----------------|:---------------|:--------------------|:--------|:----------|:-------------------|:-------------------|:--------------|:------------------|:--------------|:--------------|:--------------|:-----------|:----------------|:-----------|:------------|:--------------------|:-------|:---------|
| 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | |
| 1 | 11 |  |  |  |  |  | X | X | | X | X | | | X | X | | X | X | | X | X | X | | | | | X | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/isokaze_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T17:46:28+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T17:55:52+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of isokaze/磯風/矶风 (Azur Lane)
====================================
This is the dataset of isokaze/磯風/矶风 (Azur Lane), containing 39 images and their tags.
The core tags of this character are 'animal\_ears, green\_hair, animal\_ear\_fluff, hair\_ornament, long\_hair, green\_eyes, fang, thick\_eyebrows, bangs, tail, hair\_between\_eyes, hairband, black\_hairband, very\_long\_hair, fox\_ears', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
8545f5f2afcdc20a5319e5182fea5d6068c9cbb3 |
# Dataset Card for Evaluation run of dhanushreddy29/BrokenKeyboard
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [dhanushreddy29/BrokenKeyboard](https://huggingface.co/dhanushreddy29/BrokenKeyboard) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_dhanushreddy29__BrokenKeyboard",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T17:49:35.571074](https://huggingface.co/datasets/open-llm-leaderboard/details_dhanushreddy29__BrokenKeyboard/blob/main/results_2024-01-13T17-49-35.571074.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6639982159937499,
"acc_stderr": 0.03167193035355786,
"acc_norm": 0.6650219715139944,
"acc_norm_stderr": 0.03231409871415513,
"mc1": 0.565483476132191,
"mc1_stderr": 0.01735273874925956,
"mc2": 0.7135941358366864,
"mc2_stderr": 0.01506271807008482
},
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}
}
```
## Dataset Details
### Dataset Description
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- **Curated by:** [More Information Needed]
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### Direct Use
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## Dataset Structure
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_dhanushreddy29__BrokenKeyboard | [
"region:us"
] | 2024-01-13T17:51:49+00:00 | {"pretty_name": "Evaluation run of dhanushreddy29/BrokenKeyboard", "dataset_summary": "Dataset automatically created during the evaluation run of model [dhanushreddy29/BrokenKeyboard](https://huggingface.co/dhanushreddy29/BrokenKeyboard) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_dhanushreddy29__BrokenKeyboard\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T17:49:35.571074](https://huggingface.co/datasets/open-llm-leaderboard/details_dhanushreddy29__BrokenKeyboard/blob/main/results_2024-01-13T17-49-35.571074.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6639982159937499,\n \"acc_stderr\": 0.03167193035355786,\n \"acc_norm\": 0.6650219715139944,\n \"acc_norm_stderr\": 0.03231409871415513,\n \"mc1\": 0.565483476132191,\n \"mc1_stderr\": 0.01735273874925956,\n \"mc2\": 0.7135941358366864,\n \"mc2_stderr\": 0.01506271807008482\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6783276450511946,\n \"acc_stderr\": 0.013650488084494162,\n \"acc_norm\": 0.712457337883959,\n \"acc_norm_stderr\": 0.013226719056266127\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7103166699860586,\n \"acc_stderr\": 0.0045268830210276325,\n \"acc_norm\": 0.8833897629954193,\n \"acc_norm_stderr\": 0.003202993346991063\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237103,\n \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237103\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 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"latest", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T17-49-35.571074.parquet"]}]}, {"config_name": "harness_hendrycksTest_machine_learning_5", "data_files": [{"split": "2024_01_13T17_49_35.571074", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T17-49-35.571074.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T17-49-35.571074.parquet"]}]}, {"config_name": "harness_hendrycksTest_management_5", "data_files": [{"split": "2024_01_13T17_49_35.571074", "path": ["**/details_harness|hendrycksTest-management|5_2024-01-13T17-49-35.571074.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-management|5_2024-01-13T17-49-35.571074.parquet"]}]}, {"config_name": "harness_hendrycksTest_marketing_5", "data_files": [{"split": "2024_01_13T17_49_35.571074", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-13T17-49-35.571074.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-13T17-49-35.571074.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2024_01_13T17_49_35.571074", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T17-49-35.571074.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T17-49-35.571074.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2024_01_13T17_49_35.571074", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T17-49-35.571074.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T17-49-35.571074.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2024_01_13T17_49_35.571074", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T17-49-35.571074.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T17-49-35.571074.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2024_01_13T17_49_35.571074", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T17-49-35.571074.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T17-49-35.571074.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2024_01_13T17_49_35.571074", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T17-49-35.571074.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T17-49-35.571074.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2024_01_13T17_49_35.571074", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T17-49-35.571074.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T17-49-35.571074.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2024_01_13T17_49_35.571074", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T17-49-35.571074.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T17-49-35.571074.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2024_01_13T17_49_35.571074", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T17-49-35.571074.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T17-49-35.571074.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2024_01_13T17_49_35.571074", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T17-49-35.571074.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T17-49-35.571074.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_01_13T17_49_35.571074", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T17-49-35.571074.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T17-49-35.571074.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_01_13T17_49_35.571074", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T17-49-35.571074.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T17-49-35.571074.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_01_13T17_49_35.571074", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T17-49-35.571074.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T17-49-35.571074.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_01_13T17_49_35.571074", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T17-49-35.571074.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T17-49-35.571074.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_01_13T17_49_35.571074", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T17-49-35.571074.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T17-49-35.571074.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_01_13T17_49_35.571074", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T17-49-35.571074.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T17-49-35.571074.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_13T17_49_35.571074", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T17-49-35.571074.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T17-49-35.571074.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_13T17_49_35.571074", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T17-49-35.571074.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T17-49-35.571074.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_13T17_49_35.571074", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T17-49-35.571074.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T17-49-35.571074.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T17_49_35.571074", "path": ["**/details_harness|winogrande|5_2024-01-13T17-49-35.571074.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T17-49-35.571074.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T17_49_35.571074", "path": ["results_2024-01-13T17-49-35.571074.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T17-49-35.571074.parquet"]}]}]} | 2024-01-13T17:52:10+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of dhanushreddy29/BrokenKeyboard
Dataset automatically created during the evaluation run of model dhanushreddy29/BrokenKeyboard on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-13T17:49:35.571074(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of dhanushreddy29/BrokenKeyboard\n\n\n\nDataset automatically created during the evaluation run of model dhanushreddy29/BrokenKeyboard on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T17:49:35.571074(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of dhanushreddy29/BrokenKeyboard\n\n\n\nDataset automatically created during the evaluation run of model dhanushreddy29/BrokenKeyboard on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T17:49:35.571074(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
8facd4627dc455fe0b4a5ac68296616e3412538f |
# Dataset Card for Evaluation run of brucethemoose/Yi-34B-200K-DARE-merge-v7
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [brucethemoose/Yi-34B-200K-DARE-merge-v7](https://huggingface.co/brucethemoose/Yi-34B-200K-DARE-merge-v7) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_brucethemoose__Yi-34B-200K-DARE-merge-v7",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T18:00:33.123437](https://huggingface.co/datasets/open-llm-leaderboard/details_brucethemoose__Yi-34B-200K-DARE-merge-v7/blob/main/results_2024-01-13T18-00-33.123437.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.7681401846074161,
"acc_stderr": 0.02789845201480161,
"acc_norm": 0.7728973251356218,
"acc_norm_stderr": 0.02841712456337832,
"mc1": 0.4283965728274174,
"mc1_stderr": 0.017323088597314754,
"mc2": 0.5890169683214581,
"mc2_stderr": 0.0152246570119347
},
"harness|arc:challenge|25": {
"acc": 0.6527303754266212,
"acc_stderr": 0.013913034529620453,
"acc_norm": 0.6808873720136519,
"acc_norm_stderr": 0.013621696119173304
},
"harness|hellaswag|10": {
"acc": 0.6590320653256323,
"acc_stderr": 0.004730658073041562,
"acc_norm": 0.8598884684325832,
"acc_norm_stderr": 0.003463933286063885
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.7407407407407407,
"acc_stderr": 0.03785714465066653,
"acc_norm": 0.7407407407407407,
"acc_norm_stderr": 0.03785714465066653
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.875,
"acc_stderr": 0.026913523521537846,
"acc_norm": 0.875,
"acc_norm_stderr": 0.026913523521537846
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.77,
"acc_stderr": 0.04229525846816505,
"acc_norm": 0.77,
"acc_norm_stderr": 0.04229525846816505
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.8150943396226416,
"acc_stderr": 0.02389335183446432,
"acc_norm": 0.8150943396226416,
"acc_norm_stderr": 0.02389335183446432
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.9097222222222222,
"acc_stderr": 0.023964965777906935,
"acc_norm": 0.9097222222222222,
"acc_norm_stderr": 0.023964965777906935
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.5,
"acc_stderr": 0.050251890762960605,
"acc_norm": 0.5,
"acc_norm_stderr": 0.050251890762960605
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.62,
"acc_stderr": 0.04878317312145633,
"acc_norm": 0.62,
"acc_norm_stderr": 0.04878317312145633
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.46,
"acc_stderr": 0.05009082659620332,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.7456647398843931,
"acc_stderr": 0.0332055644308557,
"acc_norm": 0.7456647398843931,
"acc_norm_stderr": 0.0332055644308557
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.5588235294117647,
"acc_stderr": 0.049406356306056595,
"acc_norm": 0.5588235294117647,
"acc_norm_stderr": 0.049406356306056595
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.84,
"acc_stderr": 0.03684529491774709,
"acc_norm": 0.84,
"acc_norm_stderr": 0.03684529491774709
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.7829787234042553,
"acc_stderr": 0.02694748312149623,
"acc_norm": 0.7829787234042553,
"acc_norm_stderr": 0.02694748312149623
},
"harness|hendrycksTest-econometrics|5": {
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"harness|hendrycksTest-us_foreign_policy|5": {
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"harness|hendrycksTest-world_religions|5": {
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"harness|gsm8k|5": {
"acc": 0.6535253980288097,
"acc_stderr": 0.013107179054313398
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Who are the source data producers?
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#### Annotation process
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#### Who are the annotators?
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#### Personal and Sensitive Information
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## Bias, Risks, and Limitations
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_brucethemoose__Yi-34B-200K-DARE-merge-v7 | [
"region:us"
] | 2024-01-13T18:02:46+00:00 | {"pretty_name": "Evaluation run of brucethemoose/Yi-34B-200K-DARE-merge-v7", "dataset_summary": "Dataset automatically created during the evaluation run of model [brucethemoose/Yi-34B-200K-DARE-merge-v7](https://huggingface.co/brucethemoose/Yi-34B-200K-DARE-merge-v7) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_brucethemoose__Yi-34B-200K-DARE-merge-v7\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T18:00:33.123437](https://huggingface.co/datasets/open-llm-leaderboard/details_brucethemoose__Yi-34B-200K-DARE-merge-v7/blob/main/results_2024-01-13T18-00-33.123437.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.7681401846074161,\n \"acc_stderr\": 0.02789845201480161,\n \"acc_norm\": 0.7728973251356218,\n \"acc_norm_stderr\": 0.02841712456337832,\n \"mc1\": 0.4283965728274174,\n \"mc1_stderr\": 0.017323088597314754,\n \"mc2\": 0.5890169683214581,\n \"mc2_stderr\": 0.0152246570119347\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6527303754266212,\n \"acc_stderr\": 0.013913034529620453,\n \"acc_norm\": 0.6808873720136519,\n \"acc_norm_stderr\": 0.013621696119173304\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6590320653256323,\n \"acc_stderr\": 0.004730658073041562,\n \"acc_norm\": 0.8598884684325832,\n \"acc_norm_stderr\": 0.003463933286063885\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.7407407407407407,\n \"acc_stderr\": 0.03785714465066653,\n \"acc_norm\": 0.7407407407407407,\n \"acc_norm_stderr\": 0.03785714465066653\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.875,\n \"acc_stderr\": 0.026913523521537846,\n \"acc_norm\": 0.875,\n \"acc_norm_stderr\": 0.026913523521537846\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.77,\n \"acc_stderr\": 0.04229525846816505,\n \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.04229525846816505\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.8150943396226416,\n \"acc_stderr\": 0.02389335183446432,\n \"acc_norm\": 0.8150943396226416,\n \"acc_norm_stderr\": 0.02389335183446432\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.9097222222222222,\n \"acc_stderr\": 0.023964965777906935,\n \"acc_norm\": 0.9097222222222222,\n \"acc_norm_stderr\": 0.023964965777906935\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.62,\n \"acc_stderr\": 0.04878317312145633,\n \"acc_norm\": 0.62,\n \"acc_norm_stderr\": 0.04878317312145633\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7456647398843931,\n \"acc_stderr\": 0.0332055644308557,\n \"acc_norm\": 0.7456647398843931,\n \"acc_norm_stderr\": 0.0332055644308557\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.5588235294117647,\n \"acc_stderr\": 0.049406356306056595,\n \"acc_norm\": 0.5588235294117647,\n \"acc_norm_stderr\": 0.049406356306056595\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774709\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.7829787234042553,\n \"acc_stderr\": 0.02694748312149623,\n \"acc_norm\": 0.7829787234042553,\n \"acc_norm_stderr\": 0.02694748312149623\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.6052631578947368,\n \"acc_stderr\": 0.045981880578165414,\n \"acc_norm\": 0.6052631578947368,\n \"acc_norm_stderr\": 0.045981880578165414\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.7862068965517242,\n \"acc_stderr\": 0.03416520447747548,\n \"acc_norm\": 0.7862068965517242,\n \"acc_norm_stderr\": 0.03416520447747548\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.023266512213730564,\n \"acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.023266512213730564\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5476190476190477,\n \"acc_stderr\": 0.044518079590553275,\n 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["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T18-00-33.123437.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2024_01_13T18_00_33.123437", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T18-00-33.123437.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T18-00-33.123437.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2024_01_13T18_00_33.123437", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T18-00-33.123437.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T18-00-33.123437.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2024_01_13T18_00_33.123437", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T18-00-33.123437.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T18-00-33.123437.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_01_13T18_00_33.123437", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T18-00-33.123437.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T18-00-33.123437.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_01_13T18_00_33.123437", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T18-00-33.123437.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T18-00-33.123437.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_01_13T18_00_33.123437", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T18-00-33.123437.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T18-00-33.123437.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_01_13T18_00_33.123437", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T18-00-33.123437.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T18-00-33.123437.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_01_13T18_00_33.123437", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T18-00-33.123437.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T18-00-33.123437.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_01_13T18_00_33.123437", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T18-00-33.123437.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T18-00-33.123437.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_13T18_00_33.123437", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T18-00-33.123437.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T18-00-33.123437.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_13T18_00_33.123437", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T18-00-33.123437.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T18-00-33.123437.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_13T18_00_33.123437", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T18-00-33.123437.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T18-00-33.123437.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T18_00_33.123437", "path": ["**/details_harness|winogrande|5_2024-01-13T18-00-33.123437.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T18-00-33.123437.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T18_00_33.123437", "path": ["results_2024-01-13T18-00-33.123437.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T18-00-33.123437.parquet"]}]}]} | 2024-01-13T18:03:08+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of brucethemoose/Yi-34B-200K-DARE-merge-v7
Dataset automatically created during the evaluation run of model brucethemoose/Yi-34B-200K-DARE-merge-v7 on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-13T18:00:33.123437(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of brucethemoose/Yi-34B-200K-DARE-merge-v7\n\n\n\nDataset automatically created during the evaluation run of model brucethemoose/Yi-34B-200K-DARE-merge-v7 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T18:00:33.123437(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of brucethemoose/Yi-34B-200K-DARE-merge-v7\n\n\n\nDataset automatically created during the evaluation run of model brucethemoose/Yi-34B-200K-DARE-merge-v7 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T18:00:33.123437(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
f92fcd61fe1d1d9fac158e97d273f95eef6a6a66 |
# Dataset Card for Evaluation run of brucethemoose/SUS-Bagel-200K-DARE-Test
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [brucethemoose/SUS-Bagel-200K-DARE-Test](https://huggingface.co/brucethemoose/SUS-Bagel-200K-DARE-Test) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_brucethemoose__SUS-Bagel-200K-DARE-Test",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T18:09:57.188193](https://huggingface.co/datasets/open-llm-leaderboard/details_brucethemoose__SUS-Bagel-200K-DARE-Test/blob/main/results_2024-01-13T18-09-57.188193.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.7658118587114254,
"acc_stderr": 0.02808039655994379,
"acc_norm": 0.7696925363139744,
"acc_norm_stderr": 0.02861463324453946,
"mc1": 0.44920440636474906,
"mc1_stderr": 0.017412941986115305,
"mc2": 0.6119893427851197,
"mc2_stderr": 0.014925989149943244
},
"harness|arc:challenge|25": {
"acc": 0.6527303754266212,
"acc_stderr": 0.013913034529620456,
"acc_norm": 0.6808873720136519,
"acc_norm_stderr": 0.013621696119173306
},
"harness|hellaswag|10": {
"acc": 0.6566421031666999,
"acc_stderr": 0.0047385929002801905,
"acc_norm": 0.8538139812786297,
"acc_norm_stderr": 0.003525705773353417
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.49,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.49,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.7407407407407407,
"acc_stderr": 0.03785714465066652,
"acc_norm": 0.7407407407407407,
"acc_norm_stderr": 0.03785714465066652
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.8881578947368421,
"acc_stderr": 0.02564834125169361,
"acc_norm": 0.8881578947368421,
"acc_norm_stderr": 0.02564834125169361
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.79,
"acc_stderr": 0.040936018074033256,
"acc_norm": 0.79,
"acc_norm_stderr": 0.040936018074033256
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.8075471698113208,
"acc_stderr": 0.02426297983937228,
"acc_norm": 0.8075471698113208,
"acc_norm_stderr": 0.02426297983937228
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.8958333333333334,
"acc_stderr": 0.025545239210256917,
"acc_norm": 0.8958333333333334,
"acc_norm_stderr": 0.025545239210256917
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.49,
"acc_stderr": 0.05024183937956911,
"acc_norm": 0.49,
"acc_norm_stderr": 0.05024183937956911
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.64,
"acc_stderr": 0.048241815132442176,
"acc_norm": 0.64,
"acc_norm_stderr": 0.048241815132442176
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.55,
"acc_stderr": 0.05000000000000001,
"acc_norm": 0.55,
"acc_norm_stderr": 0.05000000000000001
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.7398843930635838,
"acc_stderr": 0.033450369167889904,
"acc_norm": 0.7398843930635838,
"acc_norm_stderr": 0.033450369167889904
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.5294117647058824,
"acc_stderr": 0.049665709039785295,
"acc_norm": 0.5294117647058824,
"acc_norm_stderr": 0.049665709039785295
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.82,
"acc_stderr": 0.03861229196653695,
"acc_norm": 0.82,
"acc_norm_stderr": 0.03861229196653695
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.7829787234042553,
"acc_stderr": 0.026947483121496217,
"acc_norm": 0.7829787234042553,
"acc_norm_stderr": 0.026947483121496217
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5614035087719298,
"acc_stderr": 0.04668000738510455,
"acc_norm": 0.5614035087719298,
"acc_norm_stderr": 0.04668000738510455
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.7586206896551724,
"acc_stderr": 0.03565998174135302,
"acc_norm": 0.7586206896551724,
"acc_norm_stderr": 0.03565998174135302
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.708994708994709,
"acc_stderr": 0.023393826500484875,
"acc_norm": 0.708994708994709,
"acc_norm_stderr": 0.023393826500484875
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.5793650793650794,
"acc_stderr": 0.04415438226743745,
"acc_norm": 0.5793650793650794,
"acc_norm_stderr": 0.04415438226743745
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.57,
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"acc_norm": 0.57,
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},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.9032258064516129,
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"acc_norm": 0.9032258064516129,
"acc_norm_stderr": 0.016818943416345197
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.6847290640394089,
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"acc_norm_stderr": 0.03269080871970186
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.81,
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"acc_norm": 0.81,
"acc_norm_stderr": 0.039427724440366234
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.8606060606060606,
"acc_stderr": 0.027045948825865387,
"acc_norm": 0.8606060606060606,
"acc_norm_stderr": 0.027045948825865387
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.9191919191919192,
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"acc_norm": 0.9191919191919192,
"acc_norm_stderr": 0.019417681889724536
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9792746113989638,
"acc_stderr": 0.01028141701190903,
"acc_norm": 0.9792746113989638,
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},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.8128205128205128,
"acc_stderr": 0.01977660108655004,
"acc_norm": 0.8128205128205128,
"acc_norm_stderr": 0.01977660108655004
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.45555555555555555,
"acc_stderr": 0.03036486250482443,
"acc_norm": 0.45555555555555555,
"acc_norm_stderr": 0.03036486250482443
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.8403361344537815,
"acc_stderr": 0.0237933539975288,
"acc_norm": 0.8403361344537815,
"acc_norm_stderr": 0.0237933539975288
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.4900662251655629,
"acc_stderr": 0.04081677107248437,
"acc_norm": 0.4900662251655629,
"acc_norm_stderr": 0.04081677107248437
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.9192660550458716,
"acc_stderr": 0.011680172292862088,
"acc_norm": 0.9192660550458716,
"acc_norm_stderr": 0.011680172292862088
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.6388888888888888,
"acc_stderr": 0.032757734861009996,
"acc_norm": 0.6388888888888888,
"acc_norm_stderr": 0.032757734861009996
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.9215686274509803,
"acc_stderr": 0.018869514646658928,
"acc_norm": 0.9215686274509803,
"acc_norm_stderr": 0.018869514646658928
},
"harness|hendrycksTest-high_school_world_history|5": {
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"harness|hendrycksTest-human_sexuality|5": {
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"harness|hendrycksTest-international_law|5": {
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"harness|hendrycksTest-jurisprudence|5": {
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"harness|hendrycksTest-logical_fallacies|5": {
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"harness|hendrycksTest-miscellaneous|5": {
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"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.7754189944134078,
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"harness|hendrycksTest-nutrition|5": {
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"acc_norm_stderr": 0.020823758837580912
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"harness|hendrycksTest-philosophy|5": {
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"harness|hendrycksTest-professional_accounting|5": {
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"harness|hendrycksTest-professional_psychology|5": {
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"harness|hendrycksTest-public_relations|5": {
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"acc_norm_stderr": 0.041723430387053825
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"harness|hendrycksTest-security_studies|5": {
"acc": 0.8367346938775511,
"acc_stderr": 0.02366169917709861,
"acc_norm": 0.8367346938775511,
"acc_norm_stderr": 0.02366169917709861
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"harness|hendrycksTest-sociology|5": {
"acc": 0.8955223880597015,
"acc_stderr": 0.021628920516700643,
"acc_norm": 0.8955223880597015,
"acc_norm_stderr": 0.021628920516700643
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"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.92,
"acc_stderr": 0.0272659924344291,
"acc_norm": 0.92,
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},
"harness|hendrycksTest-virology|5": {
"acc": 0.572289156626506,
"acc_stderr": 0.038515976837185335,
"acc_norm": 0.572289156626506,
"acc_norm_stderr": 0.038515976837185335
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"harness|hendrycksTest-world_religions|5": {
"acc": 0.8888888888888888,
"acc_stderr": 0.024103384202072867,
"acc_norm": 0.8888888888888888,
"acc_norm_stderr": 0.024103384202072867
},
"harness|truthfulqa:mc|0": {
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"mc2": 0.6119893427851197,
"mc2_stderr": 0.014925989149943244
},
"harness|winogrande|5": {
"acc": 0.835043409629045,
"acc_stderr": 0.010430917468237433
},
"harness|gsm8k|5": {
"acc": 0.6929492039423806,
"acc_stderr": 0.012705685723131702
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## Dataset Structure
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Annotation process
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#### Personal and Sensitive Information
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## Bias, Risks, and Limitations
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_brucethemoose__SUS-Bagel-200K-DARE-Test | [
"region:us"
] | 2024-01-13T18:12:13+00:00 | {"pretty_name": "Evaluation run of brucethemoose/SUS-Bagel-200K-DARE-Test", "dataset_summary": "Dataset automatically created during the evaluation run of model [brucethemoose/SUS-Bagel-200K-DARE-Test](https://huggingface.co/brucethemoose/SUS-Bagel-200K-DARE-Test) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_brucethemoose__SUS-Bagel-200K-DARE-Test\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T18:09:57.188193](https://huggingface.co/datasets/open-llm-leaderboard/details_brucethemoose__SUS-Bagel-200K-DARE-Test/blob/main/results_2024-01-13T18-09-57.188193.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.7658118587114254,\n \"acc_stderr\": 0.02808039655994379,\n \"acc_norm\": 0.7696925363139744,\n \"acc_norm_stderr\": 0.02861463324453946,\n \"mc1\": 0.44920440636474906,\n \"mc1_stderr\": 0.017412941986115305,\n \"mc2\": 0.6119893427851197,\n \"mc2_stderr\": 0.014925989149943244\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6527303754266212,\n \"acc_stderr\": 0.013913034529620456,\n \"acc_norm\": 0.6808873720136519,\n \"acc_norm_stderr\": 0.013621696119173306\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6566421031666999,\n \"acc_stderr\": 0.0047385929002801905,\n \"acc_norm\": 0.8538139812786297,\n \"acc_norm_stderr\": 0.003525705773353417\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.7407407407407407,\n \"acc_stderr\": 0.03785714465066652,\n \"acc_norm\": 0.7407407407407407,\n \"acc_norm_stderr\": 0.03785714465066652\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.8881578947368421,\n \"acc_stderr\": 0.02564834125169361,\n \"acc_norm\": 0.8881578947368421,\n \"acc_norm_stderr\": 0.02564834125169361\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.8075471698113208,\n \"acc_stderr\": 0.02426297983937228,\n \"acc_norm\": 0.8075471698113208,\n \"acc_norm_stderr\": 0.02426297983937228\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8958333333333334,\n \"acc_stderr\": 0.025545239210256917,\n \"acc_norm\": 0.8958333333333334,\n \"acc_norm_stderr\": 0.025545239210256917\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.64,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\": 0.64,\n \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.55,\n \"acc_stderr\": 0.05000000000000001,\n \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.05000000000000001\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7398843930635838,\n \"acc_stderr\": 0.033450369167889904,\n \"acc_norm\": 0.7398843930635838,\n \"acc_norm_stderr\": 0.033450369167889904\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.5294117647058824,\n \"acc_stderr\": 0.049665709039785295,\n \"acc_norm\": 0.5294117647058824,\n \"acc_norm_stderr\": 0.049665709039785295\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.82,\n \"acc_stderr\": 0.03861229196653695,\n \"acc_norm\": 0.82,\n \"acc_norm_stderr\": 0.03861229196653695\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.7829787234042553,\n \"acc_stderr\": 0.026947483121496217,\n \"acc_norm\": 0.7829787234042553,\n \"acc_norm_stderr\": 0.026947483121496217\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5614035087719298,\n \"acc_stderr\": 0.04668000738510455,\n \"acc_norm\": 0.5614035087719298,\n \"acc_norm_stderr\": 0.04668000738510455\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.7586206896551724,\n \"acc_stderr\": 0.03565998174135302,\n \"acc_norm\": 0.7586206896551724,\n \"acc_norm_stderr\": 0.03565998174135302\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.708994708994709,\n \"acc_stderr\": 0.023393826500484875,\n \"acc_norm\": 0.708994708994709,\n \"acc_norm_stderr\": 0.023393826500484875\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5793650793650794,\n \"acc_stderr\": 0.04415438226743745,\n \"acc_norm\": 0.5793650793650794,\n \"acc_norm_stderr\": 0.04415438226743745\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.57,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.9032258064516129,\n \"acc_stderr\": 0.016818943416345197,\n \"acc_norm\": 0.9032258064516129,\n \"acc_norm_stderr\": 0.016818943416345197\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.6847290640394089,\n \"acc_stderr\": 0.03269080871970186,\n \"acc_norm\": 0.6847290640394089,\n \"acc_norm_stderr\": 0.03269080871970186\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.81,\n \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\": 0.81,\n \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.8606060606060606,\n \"acc_stderr\": 0.027045948825865387,\n \"acc_norm\": 0.8606060606060606,\n \"acc_norm_stderr\": 0.027045948825865387\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.9191919191919192,\n \"acc_stderr\": 0.019417681889724536,\n \"acc_norm\": 0.9191919191919192,\n \"acc_norm_stderr\": 0.019417681889724536\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.9792746113989638,\n \"acc_stderr\": 0.01028141701190903,\n \"acc_norm\": 0.9792746113989638,\n \"acc_norm_stderr\": 0.01028141701190903\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.8128205128205128,\n \"acc_stderr\": 0.01977660108655004,\n \"acc_norm\": 0.8128205128205128,\n \"acc_norm_stderr\": 0.01977660108655004\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.45555555555555555,\n \"acc_stderr\": 0.03036486250482443,\n \"acc_norm\": 0.45555555555555555,\n \"acc_norm_stderr\": 0.03036486250482443\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.8403361344537815,\n \"acc_stderr\": 0.0237933539975288,\n \"acc_norm\": 0.8403361344537815,\n \"acc_norm_stderr\": 0.0237933539975288\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.4900662251655629,\n \"acc_stderr\": 0.04081677107248437,\n \"acc_norm\": 0.4900662251655629,\n \"acc_norm_stderr\": 0.04081677107248437\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.9192660550458716,\n \"acc_stderr\": 0.011680172292862088,\n \"acc_norm\": 0.9192660550458716,\n \"acc_norm_stderr\": 0.011680172292862088\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.6388888888888888,\n \"acc_stderr\": 0.032757734861009996,\n \"acc_norm\": 0.6388888888888888,\n \"acc_norm_stderr\": 0.032757734861009996\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.9215686274509803,\n \"acc_stderr\": 0.018869514646658928,\n \"acc_norm\": 0.9215686274509803,\n \"acc_norm_stderr\": 0.018869514646658928\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.9029535864978903,\n \"acc_stderr\": 0.01926932302564026,\n \"acc_norm\": 0.9029535864978903,\n \"acc_norm_stderr\": 0.01926932302564026\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.8026905829596412,\n \"acc_stderr\": 0.02670985334496796,\n \"acc_norm\": 0.8026905829596412,\n \"acc_norm_stderr\": 0.02670985334496796\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.8854961832061069,\n \"acc_stderr\": 0.027927473753597446,\n \"acc_norm\": 0.8854961832061069,\n \"acc_norm_stderr\": 0.027927473753597446\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.9008264462809917,\n \"acc_stderr\": 0.027285246312758957,\n \"acc_norm\": 0.9008264462809917,\n \"acc_norm_stderr\": 0.027285246312758957\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8981481481481481,\n \"acc_stderr\": 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["**/details_harness|truthfulqa:mc|0_2024-01-13T18-09-57.188193.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T18-09-57.188193.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T18_09_57.188193", "path": ["**/details_harness|winogrande|5_2024-01-13T18-09-57.188193.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T18-09-57.188193.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T18_09_57.188193", "path": ["results_2024-01-13T18-09-57.188193.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T18-09-57.188193.parquet"]}]}]} | 2024-01-13T18:12:35+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of brucethemoose/SUS-Bagel-200K-DARE-Test
Dataset automatically created during the evaluation run of model brucethemoose/SUS-Bagel-200K-DARE-Test on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-13T18:09:57.188193(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of brucethemoose/SUS-Bagel-200K-DARE-Test\n\n\n\nDataset automatically created during the evaluation run of model brucethemoose/SUS-Bagel-200K-DARE-Test on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T18:09:57.188193(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of brucethemoose/SUS-Bagel-200K-DARE-Test\n\n\n\nDataset automatically created during the evaluation run of model brucethemoose/SUS-Bagel-200K-DARE-Test on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T18:09:57.188193(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
acc6c60ae6638884d14269f7bc0984e43a99cbea |
# Dataset Card for Evaluation run of sequelbox/DiamondForce
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [sequelbox/DiamondForce](https://huggingface.co/sequelbox/DiamondForce) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_sequelbox__DiamondForce",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T18:13:28.839818](https://huggingface.co/datasets/open-llm-leaderboard/details_sequelbox__DiamondForce/blob/main/results_2024-01-13T18-13-28.839818.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
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"acc_norm": 0.5859341567740417,
"acc_norm_stderr": 0.03400152897884741,
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"mc1_stderr": 0.016451264440068232,
"mc2": 0.46457835926357594,
"mc2_stderr": 0.01513153294586495
},
"harness|arc:challenge|25": {
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},
"harness|hellaswag|10": {
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},
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"acc_norm": 0.34,
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},
"harness|hendrycksTest-anatomy|5": {
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"acc_norm_stderr": 0.043097329010363554
},
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"acc_norm_stderr": 0.04033565667848319
},
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"acc_norm": 0.57,
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},
"harness|hendrycksTest-clinical_knowledge|5": {
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"acc_norm": 0.6188679245283019,
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},
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},
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"acc_norm": 0.38,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-college_computer_science|5": {
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"acc_norm": 0.47,
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},
"harness|hendrycksTest-college_mathematics|5": {
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},
"harness|hendrycksTest-college_medicine|5": {
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},
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},
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"harness|hendrycksTest-high_school_geography|5": {
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"harness|hendrycksTest-high_school_psychology|5": {
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"acc_norm_stderr": 0.019794488900024117
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6545454545454545,
"acc_stderr": 0.04554619617541054,
"acc_norm": 0.6545454545454545,
"acc_norm_stderr": 0.04554619617541054
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.6571428571428571,
"acc_stderr": 0.030387262919547724,
"acc_norm": 0.6571428571428571,
"acc_norm_stderr": 0.030387262919547724
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.7761194029850746,
"acc_stderr": 0.029475250236017193,
"acc_norm": 0.7761194029850746,
"acc_norm_stderr": 0.029475250236017193
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.85,
"acc_stderr": 0.035887028128263686,
"acc_norm": 0.85,
"acc_norm_stderr": 0.035887028128263686
},
"harness|hendrycksTest-virology|5": {
"acc": 0.4819277108433735,
"acc_stderr": 0.038899512528272166,
"acc_norm": 0.4819277108433735,
"acc_norm_stderr": 0.038899512528272166
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.783625730994152,
"acc_stderr": 0.03158149539338734,
"acc_norm": 0.783625730994152,
"acc_norm_stderr": 0.03158149539338734
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3292533659730722,
"mc1_stderr": 0.016451264440068232,
"mc2": 0.46457835926357594,
"mc2_stderr": 0.01513153294586495
},
"harness|winogrande|5": {
"acc": 0.7900552486187845,
"acc_stderr": 0.01144628062926263
},
"harness|gsm8k|5": {
"acc": 0.28658074298711145,
"acc_stderr": 0.012454841668337704
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
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## Dataset Structure
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### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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[More Information Needed] | open-llm-leaderboard/details_sequelbox__DiamondForce | [
"region:us"
] | 2024-01-13T18:15:48+00:00 | {"pretty_name": "Evaluation run of sequelbox/DiamondForce", "dataset_summary": "Dataset automatically created during the evaluation run of model [sequelbox/DiamondForce](https://huggingface.co/sequelbox/DiamondForce) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_sequelbox__DiamondForce\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T18:13:28.839818](https://huggingface.co/datasets/open-llm-leaderboard/details_sequelbox__DiamondForce/blob/main/results_2024-01-13T18-13-28.839818.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.5803032096933994,\n \"acc_stderr\": 0.033300956946307504,\n \"acc_norm\": 0.5859341567740417,\n \"acc_norm_stderr\": 0.03400152897884741,\n \"mc1\": 0.3292533659730722,\n \"mc1_stderr\": 0.016451264440068232,\n \"mc2\": 0.46457835926357594,\n \"mc2_stderr\": 0.01513153294586495\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5776450511945392,\n \"acc_stderr\": 0.014434138713379981,\n \"acc_norm\": 0.621160409556314,\n \"acc_norm_stderr\": 0.014175915490000326\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.629555865365465,\n \"acc_stderr\": 0.004819367172685967,\n \"acc_norm\": 0.8342959569806812,\n \"acc_norm_stderr\": 0.0037105487209054154\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695236,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5333333333333333,\n \"acc_stderr\": 0.043097329010363554,\n \"acc_norm\": 0.5333333333333333,\n \"acc_norm_stderr\": 0.043097329010363554\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.5657894736842105,\n \"acc_stderr\": 0.04033565667848319,\n \"acc_norm\": 0.5657894736842105,\n \"acc_norm_stderr\": 0.04033565667848319\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.57,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6188679245283019,\n \"acc_stderr\": 0.029890609686286637,\n \"acc_norm\": 0.6188679245283019,\n \"acc_norm_stderr\": 0.029890609686286637\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6597222222222222,\n \"acc_stderr\": 0.039621355734862175,\n \"acc_norm\": 0.6597222222222222,\n \"acc_norm_stderr\": 0.039621355734862175\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5549132947976878,\n \"acc_stderr\": 0.03789401760283648,\n \"acc_norm\": 0.5549132947976878,\n \"acc_norm_stderr\": 0.03789401760283648\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.3137254901960784,\n \"acc_stderr\": 0.04617034827006717,\n \"acc_norm\": 0.3137254901960784,\n \"acc_norm_stderr\": 0.04617034827006717\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.4553191489361702,\n \"acc_stderr\": 0.03255525359340355,\n \"acc_norm\": 0.4553191489361702,\n \"acc_norm_stderr\": 0.03255525359340355\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2807017543859649,\n \"acc_stderr\": 0.042270544512322,\n \"acc_norm\": 0.2807017543859649,\n \"acc_norm_stderr\": 0.042270544512322\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.04164188720169375,\n \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.04164188720169375\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.30423280423280424,\n \"acc_stderr\": 0.023695415009463087,\n \"acc_norm\": 0.30423280423280424,\n \"acc_norm_stderr\": 0.023695415009463087\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.36507936507936506,\n \"acc_stderr\": 0.043062412591271526,\n \"acc_norm\": 0.36507936507936506,\n \"acc_norm_stderr\": 0.043062412591271526\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6516129032258065,\n \"acc_stderr\": 0.027104826328100944,\n \"acc_norm\": 0.6516129032258065,\n \"acc_norm_stderr\": 0.027104826328100944\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.4827586206896552,\n \"acc_stderr\": 0.035158955511656986,\n \"acc_norm\": 0.4827586206896552,\n \"acc_norm_stderr\": 0.035158955511656986\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.696969696969697,\n \"acc_stderr\": 0.03588624800091706,\n \"acc_norm\": 0.696969696969697,\n \"acc_norm_stderr\": 0.03588624800091706\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7525252525252525,\n \"acc_stderr\": 0.030746300742124498,\n \"acc_norm\": 0.7525252525252525,\n \"acc_norm_stderr\": 0.030746300742124498\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8238341968911918,\n \"acc_stderr\": 0.02749350424454806,\n \"acc_norm\": 0.8238341968911918,\n \"acc_norm_stderr\": 0.02749350424454806\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.5487179487179488,\n \"acc_stderr\": 0.025230381238934833,\n \"acc_norm\": 0.5487179487179488,\n \"acc_norm_stderr\": 0.025230381238934833\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.3148148148148148,\n \"acc_stderr\": 0.02831753349606646,\n \"acc_norm\": 0.3148148148148148,\n \"acc_norm_stderr\": 0.02831753349606646\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.5504201680672269,\n \"acc_stderr\": 0.03231293497137707,\n \"acc_norm\": 0.5504201680672269,\n \"acc_norm_stderr\": 0.03231293497137707\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.2980132450331126,\n \"acc_stderr\": 0.03734535676787198,\n \"acc_norm\": 0.2980132450331126,\n \"acc_norm_stderr\": 0.03734535676787198\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.7724770642201835,\n \"acc_stderr\": 0.017974463578776502,\n \"acc_norm\": 0.7724770642201835,\n \"acc_norm_stderr\": 0.017974463578776502\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.4444444444444444,\n \"acc_stderr\": 0.03388857118502327,\n \"acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.03388857118502327\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.7794117647058824,\n \"acc_stderr\": 0.02910225438967408,\n \"acc_norm\": 0.7794117647058824,\n \"acc_norm_stderr\": 0.02910225438967408\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7805907172995781,\n \"acc_stderr\": 0.026939106581553945,\n \"acc_norm\": 0.7805907172995781,\n \"acc_norm_stderr\": 0.026939106581553945\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6681614349775785,\n \"acc_stderr\": 0.031602951437766785,\n \"acc_norm\": 0.6681614349775785,\n \"acc_norm_stderr\": 0.031602951437766785\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.6641221374045801,\n \"acc_stderr\": 0.041423137719966634,\n \"acc_norm\": 0.6641221374045801,\n \"acc_norm_stderr\": 0.041423137719966634\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228732,\n \"acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228732\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7129629629629629,\n \"acc_stderr\": 0.043733130409147614,\n \"acc_norm\": 0.7129629629629629,\n \"acc_norm_stderr\": 0.043733130409147614\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7300613496932515,\n \"acc_stderr\": 0.034878251684978906,\n \"acc_norm\": 0.7300613496932515,\n \"acc_norm_stderr\": 0.034878251684978906\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.375,\n \"acc_stderr\": 0.04595091388086298,\n \"acc_norm\": 0.375,\n \"acc_norm_stderr\": 0.04595091388086298\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7378640776699029,\n \"acc_stderr\": 0.04354631077260594,\n \"acc_norm\": 0.7378640776699029,\n \"acc_norm_stderr\": 0.04354631077260594\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8333333333333334,\n \"acc_stderr\": 0.024414947304543688,\n \"acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.024414947304543688\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7867177522349936,\n \"acc_stderr\": 0.014648172749593517,\n \"acc_norm\": 0.7867177522349936,\n \"acc_norm_stderr\": 0.014648172749593517\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.6416184971098265,\n \"acc_stderr\": 0.0258167567915842,\n \"acc_norm\": 0.6416184971098265,\n \"acc_norm_stderr\": 0.0258167567915842\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.43575418994413406,\n \"acc_stderr\": 0.016583881958602394,\n \"acc_norm\": 0.43575418994413406,\n \"acc_norm_stderr\": 0.016583881958602394\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.6274509803921569,\n \"acc_stderr\": 0.027684181883302898,\n \"acc_norm\": 0.6274509803921569,\n \"acc_norm_stderr\": 0.027684181883302898\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6591639871382636,\n \"acc_stderr\": 0.026920841260776162,\n \"acc_norm\": 0.6591639871382636,\n \"acc_norm_stderr\": 0.026920841260776162\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.7160493827160493,\n \"acc_stderr\": 0.025089478523765134,\n \"acc_norm\": 0.7160493827160493,\n \"acc_norm_stderr\": 0.025089478523765134\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.4078014184397163,\n \"acc_stderr\": 0.029316011776343555,\n \"acc_norm\": 0.4078014184397163,\n \"acc_norm_stderr\": 0.029316011776343555\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.43741851368970014,\n \"acc_stderr\": 0.012669813464935726,\n \"acc_norm\": 0.43741851368970014,\n \"acc_norm_stderr\": 0.012669813464935726\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.5625,\n \"acc_stderr\": 0.030134614954403924,\n \"acc_norm\": 0.5625,\n \"acc_norm_stderr\": 0.030134614954403924\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.6029411764705882,\n \"acc_stderr\": 0.019794488900024117,\n \"acc_norm\": 0.6029411764705882,\n \"acc_norm_stderr\": 0.019794488900024117\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6545454545454545,\n \"acc_stderr\": 0.04554619617541054,\n \"acc_norm\": 0.6545454545454545,\n \"acc_norm_stderr\": 0.04554619617541054\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.6571428571428571,\n \"acc_stderr\": 0.030387262919547724,\n \"acc_norm\": 0.6571428571428571,\n \"acc_norm_stderr\": 0.030387262919547724\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7761194029850746,\n \"acc_stderr\": 0.029475250236017193,\n \"acc_norm\": 0.7761194029850746,\n \"acc_norm_stderr\": 0.029475250236017193\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.85,\n \"acc_stderr\": 0.035887028128263686,\n \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.035887028128263686\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4819277108433735,\n \"acc_stderr\": 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#region-us
|
# Dataset Card for Evaluation run of sequelbox/DiamondForce
Dataset automatically created during the evaluation run of model sequelbox/DiamondForce on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-13T18:13:28.839818(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of sequelbox/DiamondForce\n\n\n\nDataset automatically created during the evaluation run of model sequelbox/DiamondForce on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T18:13:28.839818(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of sequelbox/DiamondForce\n\n\n\nDataset automatically created during the evaluation run of model sequelbox/DiamondForce on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T18:13:28.839818(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
4125ccc670cbb241b465cc070fb7304923e671e3 |
# Dataset of hatsuzuki/初月/初月 (Azur Lane)
This is the dataset of hatsuzuki/初月/初月 (Azur Lane), containing 41 images and their tags.
The core tags of this character are `black_hair, long_hair, red_eyes, breasts, bangs, red_hair, multicolored_hair, horns, twintails, small_breasts, two-tone_hair, very_long_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 41 | 76.16 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hatsuzuki_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 41 | 33.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hatsuzuki_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 108 | 77.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hatsuzuki_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 41 | 61.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hatsuzuki_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 108 | 121.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hatsuzuki_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/hatsuzuki_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 12 |  |  |  |  |  | 1girl, looking_at_viewer, solo, detached_sleeves, open_mouth, wide_sleeves, black_pantyhose, bare_shoulders, smile, white_background, cleavage, katana, simple_background, holding_sword, japanese_clothes, skirt |
| 1 | 12 |  |  |  |  |  | 1girl, long_sleeves, looking_at_viewer, navel, solo, black_bikini, thigh_strap, black_choker, open_mouth, open_shirt, sitting, stomach, blush, innertube, white_shirt, collarbone, see-through, simple_background, thighs, water, white_background, :d, barefoot, medium_breasts |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | detached_sleeves | open_mouth | wide_sleeves | black_pantyhose | bare_shoulders | smile | white_background | cleavage | katana | simple_background | holding_sword | japanese_clothes | skirt | long_sleeves | navel | black_bikini | thigh_strap | black_choker | open_shirt | sitting | stomach | blush | innertube | white_shirt | collarbone | see-through | thighs | water | :d | barefoot | medium_breasts |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:-------------------|:-------------|:---------------|:------------------|:-----------------|:--------|:-------------------|:-----------|:---------|:--------------------|:----------------|:-------------------|:--------|:---------------|:--------|:---------------|:--------------|:---------------|:-------------|:----------|:----------|:--------|:------------|:--------------|:-------------|:--------------|:---------|:--------|:-----|:-----------|:-----------------|
| 0 | 12 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | |
| 1 | 12 |  |  |  |  |  | X | X | X | | X | | | | | X | | | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/hatsuzuki_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T18:24:24+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T18:33:49+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of hatsuzuki/初月/初月 (Azur Lane)
======================================
This is the dataset of hatsuzuki/初月/初月 (Azur Lane), containing 41 images and their tags.
The core tags of this character are 'black\_hair, long\_hair, red\_eyes, breasts, bangs, red\_hair, multicolored\_hair, horns, twintails, small\_breasts, two-tone\_hair, very\_long\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
fc6c64263c59939e0ca5c016bd69ede6cbc77407 |
# Dataset of gridley/グリッドレイ/格里德利 (Azur Lane)
This is the dataset of gridley/グリッドレイ/格里德利 (Azur Lane), containing 12 images and their tags.
The core tags of this character are `blonde_hair, blue_eyes, bangs, hair_between_eyes, ahoge, long_hair, bow, hair_ornament, two_side_up, drill_hair, red_bow, animal_ears, deer_ears, ribbon`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 12 | 16.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gridley_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 12 | 9.37 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gridley_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 30 | 20.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gridley_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 12 | 14.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gridley_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 30 | 29.22 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gridley_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/gridley_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 12 |  |  |  |  |  | blush, 1girl, bare_shoulders, looking_at_viewer, smile, solo, holding, open_mouth, sleeveless, thighhighs, camera, christmas, red_dress, reindeer_antlers, santa_costume, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | blush | 1girl | bare_shoulders | looking_at_viewer | smile | solo | holding | open_mouth | sleeveless | thighhighs | camera | christmas | red_dress | reindeer_antlers | santa_costume | white_background |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-----------------|:--------------------|:--------|:-------|:----------|:-------------|:-------------|:-------------|:---------|:------------|:------------|:-------------------|:----------------|:-------------------|
| 0 | 12 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/gridley_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T18:24:40+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T18:30:46+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of gridley/グリッドレイ/格里德利 (Azur Lane)
==========================================
This is the dataset of gridley/グリッドレイ/格里德利 (Azur Lane), containing 12 images and their tags.
The core tags of this character are 'blonde\_hair, blue\_eyes, bangs, hair\_between\_eyes, ahoge, long\_hair, bow, hair\_ornament, two\_side\_up, drill\_hair, red\_bow, animal\_ears, deer\_ears, ribbon', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
3c873a5d9d6dc4fe432bcf7648e02a4fb64bbb59 |
# Dataset Card for Evaluation run of aari1995/germeo-7b-laser
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [aari1995/germeo-7b-laser](https://huggingface.co/aari1995/germeo-7b-laser) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_aari1995__germeo-7b-laser",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T18:27:49.824954](https://huggingface.co/datasets/open-llm-leaderboard/details_aari1995__germeo-7b-laser/blob/main/results_2024-01-13T18-27-49.824954.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6055285169834799,
"acc_stderr": 0.033079665720799664,
"acc_norm": 0.6095438527185658,
"acc_norm_stderr": 0.03374506182230424,
"mc1": 0.3733170134638923,
"mc1_stderr": 0.016932370557570634,
"mc2": 0.5382753959859625,
"mc2_stderr": 0.01572725969894502
},
"harness|arc:challenge|25": {
"acc": 0.5784982935153583,
"acc_stderr": 0.014430197069326023,
"acc_norm": 0.6075085324232082,
"acc_norm_stderr": 0.014269634635670728
},
"harness|hellaswag|10": {
"acc": 0.6415056761601274,
"acc_stderr": 0.004785781979354868,
"acc_norm": 0.8281218880701056,
"acc_norm_stderr": 0.003765034286153438
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.32,
"acc_stderr": 0.04688261722621503,
"acc_norm": 0.32,
"acc_norm_stderr": 0.04688261722621503
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5703703703703704,
"acc_stderr": 0.042763494943765995,
"acc_norm": 0.5703703703703704,
"acc_norm_stderr": 0.042763494943765995
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6381578947368421,
"acc_stderr": 0.03910525752849724,
"acc_norm": 0.6381578947368421,
"acc_norm_stderr": 0.03910525752849724
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.55,
"acc_stderr": 0.05,
"acc_norm": 0.55,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6641509433962264,
"acc_stderr": 0.02906722014664483,
"acc_norm": 0.6641509433962264,
"acc_norm_stderr": 0.02906722014664483
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6666666666666666,
"acc_stderr": 0.03942082639927213,
"acc_norm": 0.6666666666666666,
"acc_norm_stderr": 0.03942082639927213
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.47,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.47,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.45,
"acc_stderr": 0.05,
"acc_norm": 0.45,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.27,
"acc_stderr": 0.0446196043338474,
"acc_norm": 0.27,
"acc_norm_stderr": 0.0446196043338474
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5780346820809249,
"acc_stderr": 0.0376574669386515,
"acc_norm": 0.5780346820809249,
"acc_norm_stderr": 0.0376574669386515
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.39215686274509803,
"acc_stderr": 0.048580835742663454,
"acc_norm": 0.39215686274509803,
"acc_norm_stderr": 0.048580835742663454
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.76,
"acc_stderr": 0.04292346959909283,
"acc_norm": 0.76,
"acc_norm_stderr": 0.04292346959909283
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5319148936170213,
"acc_stderr": 0.03261936918467382,
"acc_norm": 0.5319148936170213,
"acc_norm_stderr": 0.03261936918467382
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4649122807017544,
"acc_stderr": 0.046920083813689104,
"acc_norm": 0.4649122807017544,
"acc_norm_stderr": 0.046920083813689104
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5448275862068965,
"acc_stderr": 0.04149886942192117,
"acc_norm": 0.5448275862068965,
"acc_norm_stderr": 0.04149886942192117
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.42592592592592593,
"acc_stderr": 0.02546714904546955,
"acc_norm": 0.42592592592592593,
"acc_norm_stderr": 0.02546714904546955
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.373015873015873,
"acc_stderr": 0.04325506042017086,
"acc_norm": 0.373015873015873,
"acc_norm_stderr": 0.04325506042017086
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.37,
"acc_stderr": 0.048523658709391,
"acc_norm": 0.37,
"acc_norm_stderr": 0.048523658709391
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7387096774193549,
"acc_stderr": 0.02499305339776482,
"acc_norm": 0.7387096774193549,
"acc_norm_stderr": 0.02499305339776482
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.46798029556650245,
"acc_stderr": 0.035107665979592154,
"acc_norm": 0.46798029556650245,
"acc_norm_stderr": 0.035107665979592154
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.65,
"acc_stderr": 0.047937248544110196,
"acc_norm": 0.65,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7757575757575758,
"acc_stderr": 0.03256866661681102,
"acc_norm": 0.7757575757575758,
"acc_norm_stderr": 0.03256866661681102
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7474747474747475,
"acc_stderr": 0.030954055470365897,
"acc_norm": 0.7474747474747475,
"acc_norm_stderr": 0.030954055470365897
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8601036269430051,
"acc_stderr": 0.02503387058301518,
"acc_norm": 0.8601036269430051,
"acc_norm_stderr": 0.02503387058301518
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6205128205128205,
"acc_stderr": 0.024603626924097424,
"acc_norm": 0.6205128205128205,
"acc_norm_stderr": 0.024603626924097424
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3148148148148148,
"acc_stderr": 0.02831753349606648,
"acc_norm": 0.3148148148148148,
"acc_norm_stderr": 0.02831753349606648
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.680672268907563,
"acc_stderr": 0.030283995525884396,
"acc_norm": 0.680672268907563,
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"harness|gsm8k|5": {
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"acc_stderr": 0.013650728047064685
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Who are the source data producers?
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#### Annotation process
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#### Personal and Sensitive Information
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## Bias, Risks, and Limitations
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_aari1995__germeo-7b-laser | [
"region:us"
] | 2024-01-13T18:30:09+00:00 | {"pretty_name": "Evaluation run of aari1995/germeo-7b-laser", "dataset_summary": "Dataset automatically created during the evaluation run of model [aari1995/germeo-7b-laser](https://huggingface.co/aari1995/germeo-7b-laser) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_aari1995__germeo-7b-laser\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T18:27:49.824954](https://huggingface.co/datasets/open-llm-leaderboard/details_aari1995__germeo-7b-laser/blob/main/results_2024-01-13T18-27-49.824954.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6055285169834799,\n \"acc_stderr\": 0.033079665720799664,\n \"acc_norm\": 0.6095438527185658,\n \"acc_norm_stderr\": 0.03374506182230424,\n \"mc1\": 0.3733170134638923,\n \"mc1_stderr\": 0.016932370557570634,\n \"mc2\": 0.5382753959859625,\n \"mc2_stderr\": 0.01572725969894502\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5784982935153583,\n \"acc_stderr\": 0.014430197069326023,\n \"acc_norm\": 0.6075085324232082,\n \"acc_norm_stderr\": 0.014269634635670728\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6415056761601274,\n \"acc_stderr\": 0.004785781979354868,\n \"acc_norm\": 0.8281218880701056,\n \"acc_norm_stderr\": 0.003765034286153438\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621503,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621503\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5703703703703704,\n \"acc_stderr\": 0.042763494943765995,\n \"acc_norm\": 0.5703703703703704,\n \"acc_norm_stderr\": 0.042763494943765995\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6381578947368421,\n \"acc_stderr\": 0.03910525752849724,\n \"acc_norm\": 0.6381578947368421,\n \"acc_norm_stderr\": 0.03910525752849724\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6641509433962264,\n \"acc_stderr\": 0.02906722014664483,\n \"acc_norm\": 0.6641509433962264,\n \"acc_norm_stderr\": 0.02906722014664483\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.03942082639927213,\n \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.03942082639927213\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5780346820809249,\n \"acc_stderr\": 0.0376574669386515,\n \"acc_norm\": 0.5780346820809249,\n \"acc_norm_stderr\": 0.0376574669386515\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.39215686274509803,\n \"acc_stderr\": 0.048580835742663454,\n \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.048580835742663454\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.76,\n \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5319148936170213,\n \"acc_stderr\": 0.03261936918467382,\n \"acc_norm\": 0.5319148936170213,\n \"acc_norm_stderr\": 0.03261936918467382\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4649122807017544,\n \"acc_stderr\": 0.046920083813689104,\n \"acc_norm\": 0.4649122807017544,\n \"acc_norm_stderr\": 0.046920083813689104\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192117,\n \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192117\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.42592592592592593,\n \"acc_stderr\": 0.02546714904546955,\n \"acc_norm\": 0.42592592592592593,\n \"acc_norm_stderr\": 0.02546714904546955\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.373015873015873,\n \"acc_stderr\": 0.04325506042017086,\n \"acc_norm\": 0.373015873015873,\n \"acc_norm_stderr\": 0.04325506042017086\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7387096774193549,\n \"acc_stderr\": 0.02499305339776482,\n \"acc_norm\": 0.7387096774193549,\n \"acc_norm_stderr\": 0.02499305339776482\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.46798029556650245,\n \"acc_stderr\": 0.035107665979592154,\n \"acc_norm\": 0.46798029556650245,\n \"acc_norm_stderr\": 0.035107665979592154\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.65,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.03256866661681102,\n \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.03256866661681102\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7474747474747475,\n \"acc_stderr\": 0.030954055470365897,\n \"acc_norm\": 0.7474747474747475,\n \"acc_norm_stderr\": 0.030954055470365897\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8601036269430051,\n \"acc_stderr\": 0.02503387058301518,\n \"acc_norm\": 0.8601036269430051,\n \"acc_norm_stderr\": 0.02503387058301518\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6205128205128205,\n \"acc_stderr\": 0.024603626924097424,\n \"acc_norm\": 0.6205128205128205,\n \"acc_norm_stderr\": 0.024603626924097424\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.3148148148148148,\n \"acc_stderr\": 0.02831753349606648,\n \"acc_norm\": 0.3148148148148148,\n \"acc_norm_stderr\": 0.02831753349606648\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.680672268907563,\n \"acc_stderr\": 0.030283995525884396,\n \"acc_norm\": 0.680672268907563,\n \"acc_norm_stderr\": 0.030283995525884396\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.3443708609271523,\n \"acc_stderr\": 0.03879687024073327,\n \"acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.03879687024073327\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8036697247706422,\n \"acc_stderr\": 0.017030719339154333,\n \"acc_norm\": 0.8036697247706422,\n \"acc_norm_stderr\": 0.017030719339154333\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.46296296296296297,\n \"acc_stderr\": 0.03400603625538272,\n \"acc_norm\": 0.46296296296296297,\n \"acc_norm_stderr\": 0.03400603625538272\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.7549019607843137,\n \"acc_stderr\": 0.03019028245350195,\n \"acc_norm\": 0.7549019607843137,\n \"acc_norm_stderr\": 0.03019028245350195\n },\n 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["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T18-27-49.824954.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T18-27-49.824954.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_01_13T18_27_49.824954", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T18-27-49.824954.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T18-27-49.824954.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_01_13T18_27_49.824954", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T18-27-49.824954.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T18-27-49.824954.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_01_13T18_27_49.824954", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T18-27-49.824954.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T18-27-49.824954.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_13T18_27_49.824954", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T18-27-49.824954.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T18-27-49.824954.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_13T18_27_49.824954", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T18-27-49.824954.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T18-27-49.824954.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_13T18_27_49.824954", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T18-27-49.824954.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T18-27-49.824954.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T18_27_49.824954", "path": ["**/details_harness|winogrande|5_2024-01-13T18-27-49.824954.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T18-27-49.824954.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T18_27_49.824954", "path": ["results_2024-01-13T18-27-49.824954.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T18-27-49.824954.parquet"]}]}]} | 2024-01-13T18:30:31+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of aari1995/germeo-7b-laser
Dataset automatically created during the evaluation run of model aari1995/germeo-7b-laser on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-13T18:27:49.824954(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of aari1995/germeo-7b-laser\n\n\n\nDataset automatically created during the evaluation run of model aari1995/germeo-7b-laser on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T18:27:49.824954(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of aari1995/germeo-7b-laser\n\n\n\nDataset automatically created during the evaluation run of model aari1995/germeo-7b-laser on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T18:27:49.824954(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
48a338f806088c9430e36f608e81a7818004399d |
# Dataset of blucher/ブリュッヒャー/布吕歇尔 (Azur Lane)
This is the dataset of blucher/ブリュッヒャー/布吕歇尔 (Azur Lane), containing 40 images and their tags.
The core tags of this character are `long_hair, blonde_hair, red_eyes, breasts, ahoge, bangs, twintails, fang, large_breasts, skin_fang`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 40 | 60.05 MiB | [Download](https://huggingface.co/datasets/CyberHarem/blucher_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 40 | 31.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/blucher_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 104 | 72.70 MiB | [Download](https://huggingface.co/datasets/CyberHarem/blucher_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 40 | 52.51 MiB | [Download](https://huggingface.co/datasets/CyberHarem/blucher_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 104 | 104.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/blucher_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/blucher_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 20 |  |  |  |  |  | smile, 1girl, looking_at_viewer, solo, open_mouth, blush, black_gloves, red_scarf, red_skirt, black_thighhighs, fingerless_gloves, white_background, hair_between_eyes, plaid_skirt, simple_background, pleated_skirt |
| 1 | 7 |  |  |  |  |  | 1girl, bodysuit, goggles_on_head, looking_at_viewer, smile, solo, ass, fake_tail, long_sleeves, official_alternate_costume, rabbit_tail, sideboob, cropped_jacket, open_mouth, white_jacket, bandaid_on_face, blush, from_behind, blue_sky, day, full_body, medium_breasts, outdoors, shoes, snow, white_gloves |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | smile | 1girl | looking_at_viewer | solo | open_mouth | blush | black_gloves | red_scarf | red_skirt | black_thighhighs | fingerless_gloves | white_background | hair_between_eyes | plaid_skirt | simple_background | pleated_skirt | bodysuit | goggles_on_head | ass | fake_tail | long_sleeves | official_alternate_costume | rabbit_tail | sideboob | cropped_jacket | white_jacket | bandaid_on_face | from_behind | blue_sky | day | full_body | medium_breasts | outdoors | shoes | snow | white_gloves |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:--------------------|:-------|:-------------|:--------|:---------------|:------------|:------------|:-------------------|:--------------------|:-------------------|:--------------------|:--------------|:--------------------|:----------------|:-----------|:------------------|:------|:------------|:---------------|:-----------------------------|:--------------|:-----------|:-----------------|:---------------|:------------------|:--------------|:-----------|:------|:------------|:-----------------|:-----------|:--------|:-------|:---------------|
| 0 | 20 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | |
| 1 | 7 |  |  |  |  |  | X | X | X | X | X | X | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/blucher_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T18:43:00+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T18:52:07+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of blucher/ブリュッヒャー/布吕歇尔 (Azur Lane)
===========================================
This is the dataset of blucher/ブリュッヒャー/布吕歇尔 (Azur Lane), containing 40 images and their tags.
The core tags of this character are 'long\_hair, blonde\_hair, red\_eyes, breasts, ahoge, bangs, twintails, fang, large\_breasts, skin\_fang', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
cdee76c11f4d6feb0379bda51c287820b8741bb6 |
# Dataset of oklahoma/オクラホマ/俄克拉荷马 (Azur Lane)
This is the dataset of oklahoma/オクラホマ/俄克拉荷马 (Azur Lane), containing 28 images and their tags.
The core tags of this character are `ahoge, blue_eyes, breasts, hair_between_eyes, blonde_hair, short_hair, bangs, large_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 28 | 34.87 MiB | [Download](https://huggingface.co/datasets/CyberHarem/oklahoma_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 28 | 18.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/oklahoma_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 65 | 39.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/oklahoma_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 28 | 30.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/oklahoma_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 65 | 61.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/oklahoma_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/oklahoma_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 8 |  |  |  |  |  | 1girl, looking_at_viewer, solo, detached_sleeves, open_mouth, hat, simple_background, :d, boots, brown_gloves, white_background, brown_skirt, cleavage_cutout, long_sleeves, medium_breasts |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | detached_sleeves | open_mouth | hat | simple_background | :d | boots | brown_gloves | white_background | brown_skirt | cleavage_cutout | long_sleeves | medium_breasts |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:-------------------|:-------------|:------|:--------------------|:-----|:--------|:---------------|:-------------------|:--------------|:------------------|:---------------|:-----------------|
| 0 | 8 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/oklahoma_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T18:43:02+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T18:51:35+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of oklahoma/オクラホマ/俄克拉荷马 (Azur Lane)
===========================================
This is the dataset of oklahoma/オクラホマ/俄克拉荷马 (Azur Lane), containing 28 images and their tags.
The core tags of this character are 'ahoge, blue\_eyes, breasts, hair\_between\_eyes, blonde\_hair, short\_hair, bangs, large\_breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
148a663f07bffb3e1d963185be89795740a3209d |
# Dataset of mccall/マッコール/麦考尔 (Azur Lane)
This is the dataset of mccall/マッコール/麦考尔 (Azur Lane), containing 12 images and their tags.
The core tags of this character are `blue_eyes, hair_ornament, long_hair, ahoge, pink_hair, star_hair_ornament, twintails, low_twintails, hairclip, bangs, hair_between_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 12 | 8.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mccall_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 12 | 6.03 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mccall_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 25 | 12.03 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mccall_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 12 | 7.66 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mccall_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 25 | 15.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mccall_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/mccall_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 12 |  |  |  |  |  | 1girl, blush, star_(symbol), popsicle, looking_at_viewer, solo, holding, shoes, short_sleeves, white_background, dress, sailor_collar |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | star_(symbol) | popsicle | looking_at_viewer | solo | holding | shoes | short_sleeves | white_background | dress | sailor_collar |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:----------------|:-----------|:--------------------|:-------|:----------|:--------|:----------------|:-------------------|:--------|:----------------|
| 0 | 12 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/mccall_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T18:43:03+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T18:46:48+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of mccall/マッコール/麦考尔 (Azur Lane)
=======================================
This is the dataset of mccall/マッコール/麦考尔 (Azur Lane), containing 12 images and their tags.
The core tags of this character are 'blue\_eyes, hair\_ornament, long\_hair, ahoge, pink\_hair, star\_hair\_ornament, twintails, low\_twintails, hairclip, bangs, hair\_between\_eyes', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
b20a0a097e3c9e213353cf3789d9b319c5a16049 |
# Dataset of kiev/キエフ/基辅 (Azur Lane)
This is the dataset of kiev/キエフ/基辅 (Azur Lane), containing 69 images and their tags.
The core tags of this character are `long_hair, red_eyes, twintails, breasts, hair_bun, hair_over_one_eye, cone_hair_bun, white_hair, very_long_hair, small_breasts, hat, medium_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 69 | 124.83 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kiev_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 69 | 58.66 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kiev_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 177 | 135.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kiev_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 69 | 105.86 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kiev_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 177 | 214.41 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kiev_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/kiev_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 24 |  |  |  |  |  | 1girl, bare_shoulders, solo, white_dress, looking_at_viewer, fur-trimmed_dress, very_long_sleeves, one_eye_covered, pom_pom_hair_ornament, criss-cross_halter, cleavage, fur_hat, thighhighs, white_headwear, standing, sleeves_past_wrists |
| 1 | 23 |  |  |  |  |  | looking_at_viewer, 1girl, official_alternate_costume, bare_shoulders, solo, one_eye_covered, dress, elbow_gloves, black_gloves, blush, navel_cutout, ribbon, simple_background, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | solo | white_dress | looking_at_viewer | fur-trimmed_dress | very_long_sleeves | one_eye_covered | pom_pom_hair_ornament | criss-cross_halter | cleavage | fur_hat | thighhighs | white_headwear | standing | sleeves_past_wrists | official_alternate_costume | dress | elbow_gloves | black_gloves | blush | navel_cutout | ribbon | simple_background | white_background |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:-------|:--------------|:--------------------|:--------------------|:--------------------|:------------------|:------------------------|:---------------------|:-----------|:----------|:-------------|:-----------------|:-----------|:----------------------|:-----------------------------|:--------|:---------------|:---------------|:--------|:---------------|:---------|:--------------------|:-------------------|
| 0 | 24 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | |
| 1 | 23 |  |  |  |  |  | X | X | X | | X | | | X | | | | | | | | | X | X | X | X | X | X | X | X | X |
| CyberHarem/kiev_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T18:43:09+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T19:03:09+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of kiev/キエフ/基辅 (Azur Lane)
==================================
This is the dataset of kiev/キエフ/基辅 (Azur Lane), containing 69 images and their tags.
The core tags of this character are 'long\_hair, red\_eyes, twintails, breasts, hair\_bun, hair\_over\_one\_eye, cone\_hair\_bun, white\_hair, very\_long\_hair, small\_breasts, hat, medium\_breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
29e0ad7a719f3baa7116b379734fd1f5cfd6f8a7 |
This is a filtered version of Philip May's German paraphrase dataset.
The dataset has been filtered for the sake of convenience, since smaller devices do not support such large files.
All text pairs in the dataset are paraphrases, and are therefore labelled 1. As such, the dataset is well-suited for use in conjunction with the multiple negatives ranking loss.
As the original author suggests, the dataset has been filtered, mostly following the guidelines set by the author. Any row that doesn't comply with the following conditions was filtered out:
- min_char_len < 25
- de_token_count > 30
- en_de_token_count > 30
- jaccard_similarity > 0.3
- cos_sim < 0.9
## Licensing
Copyright (c) 2022 [Philip May](https://may.la/), [Deutsche Telekom AG](https://www.telekom.com/).
This work is licensed under [CC-BY-SA 4.0]([https://link-url-here.org](https://creativecommons.org/licenses/by-sa/4.0/). | danielheinz/telekom-backtrans-paraphrase-filtered | [
"task_categories:feature-extraction",
"task_categories:text-classification",
"size_categories:100K<n<1M",
"language:de",
"license:cc-by-sa-4.0",
"region:us"
] | 2024-01-13T18:51:54+00:00 | {"language": ["de"], "license": "cc-by-sa-4.0", "size_categories": ["100K<n<1M"], "task_categories": ["feature-extraction", "text-classification"]} | 2024-01-13T22:01:06+00:00 | [] | [
"de"
] | TAGS
#task_categories-feature-extraction #task_categories-text-classification #size_categories-100K<n<1M #language-German #license-cc-by-sa-4.0 #region-us
|
This is a filtered version of Philip May's German paraphrase dataset.
The dataset has been filtered for the sake of convenience, since smaller devices do not support such large files.
All text pairs in the dataset are paraphrases, and are therefore labelled 1. As such, the dataset is well-suited for use in conjunction with the multiple negatives ranking loss.
As the original author suggests, the dataset has been filtered, mostly following the guidelines set by the author. Any row that doesn't comply with the following conditions was filtered out:
- min_char_len < 25
- de_token_count > 30
- en_de_token_count > 30
- jaccard_similarity > 0.3
- cos_sim < 0.9
## Licensing
Copyright (c) 2022 Philip May, Deutsche Telekom AG.
This work is licensed under CC-BY-SA 4.0. | [
"## Licensing\n\nCopyright (c) 2022 Philip May, Deutsche Telekom AG.\n\nThis work is licensed under CC-BY-SA 4.0."
] | [
"TAGS\n#task_categories-feature-extraction #task_categories-text-classification #size_categories-100K<n<1M #language-German #license-cc-by-sa-4.0 #region-us \n",
"## Licensing\n\nCopyright (c) 2022 Philip May, Deutsche Telekom AG.\n\nThis work is licensed under CC-BY-SA 4.0."
] |
a6594457a6546e8ae0a2757fff970bd2ac977f2a |
# Dataset Card for Evaluation run of Unbabel/TowerBase-7B-v0.1
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Unbabel/TowerBase-7B-v0.1](https://huggingface.co/Unbabel/TowerBase-7B-v0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Unbabel__TowerBase-7B-v0.1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T18:50:27.460863](https://huggingface.co/datasets/open-llm-leaderboard/details_Unbabel__TowerBase-7B-v0.1/blob/main/results_2024-01-13T18-50-27.460863.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.4376530652603909,
"acc_stderr": 0.03443246082169724,
"acc_norm": 0.4418549088967034,
"acc_norm_stderr": 0.035215274463337505,
"mc1": 0.23745410036719705,
"mc1_stderr": 0.014896277441041843,
"mc2": 0.3729251403943506,
"mc2_stderr": 0.013360710032144346
},
"harness|arc:challenge|25": {
"acc": 0.48464163822525597,
"acc_stderr": 0.014604496129394906,
"acc_norm": 0.5102389078498294,
"acc_norm_stderr": 0.014608326906285012
},
"harness|hellaswag|10": {
"acc": 0.5780720971917944,
"acc_stderr": 0.004928578106026371,
"acc_norm": 0.7768372834096794,
"acc_norm_stderr": 0.0041551563175093375
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542128,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542128
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.4148148148148148,
"acc_stderr": 0.04256193767901407,
"acc_norm": 0.4148148148148148,
"acc_norm_stderr": 0.04256193767901407
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.39473684210526316,
"acc_stderr": 0.039777499346220734,
"acc_norm": 0.39473684210526316,
"acc_norm_stderr": 0.039777499346220734
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.4226415094339623,
"acc_stderr": 0.030402331445769537,
"acc_norm": 0.4226415094339623,
"acc_norm_stderr": 0.030402331445769537
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.4305555555555556,
"acc_stderr": 0.04140685639111503,
"acc_norm": 0.4305555555555556,
"acc_norm_stderr": 0.04140685639111503
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.32,
"acc_stderr": 0.04688261722621504,
"acc_norm": 0.32,
"acc_norm_stderr": 0.04688261722621504
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.4,
"acc_stderr": 0.04923659639173309,
"acc_norm": 0.4,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542127,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542127
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.4277456647398844,
"acc_stderr": 0.037724468575180255,
"acc_norm": 0.4277456647398844,
"acc_norm_stderr": 0.037724468575180255
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.18627450980392157,
"acc_stderr": 0.038739587141493524,
"acc_norm": 0.18627450980392157,
"acc_norm_stderr": 0.038739587141493524
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.49,
"acc_stderr": 0.05024183937956911,
"acc_norm": 0.49,
"acc_norm_stderr": 0.05024183937956911
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.37872340425531914,
"acc_stderr": 0.03170995606040655,
"acc_norm": 0.37872340425531914,
"acc_norm_stderr": 0.03170995606040655
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.3333333333333333,
"acc_stderr": 0.044346007015849245,
"acc_norm": 0.3333333333333333,
"acc_norm_stderr": 0.044346007015849245
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.42758620689655175,
"acc_stderr": 0.04122737111370331,
"acc_norm": 0.42758620689655175,
"acc_norm_stderr": 0.04122737111370331
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.2804232804232804,
"acc_stderr": 0.02313528797432563,
"acc_norm": 0.2804232804232804,
"acc_norm_stderr": 0.02313528797432563
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.23809523809523808,
"acc_stderr": 0.038095238095238106,
"acc_norm": 0.23809523809523808,
"acc_norm_stderr": 0.038095238095238106
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.29,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.29,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.432258064516129,
"acc_stderr": 0.02818173972001941,
"acc_norm": 0.432258064516129,
"acc_norm_stderr": 0.02818173972001941
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.2955665024630542,
"acc_stderr": 0.032104944337514575,
"acc_norm": 0.2955665024630542,
"acc_norm_stderr": 0.032104944337514575
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.44,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.44,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.503030303030303,
"acc_stderr": 0.03904272341431856,
"acc_norm": 0.503030303030303,
"acc_norm_stderr": 0.03904272341431856
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.4595959595959596,
"acc_stderr": 0.035507024651313425,
"acc_norm": 0.4595959595959596,
"acc_norm_stderr": 0.035507024651313425
},
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"harness|gsm8k|5": {
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"acc_stderr": 0.009298499235587877
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Who are the source data producers?
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#### Annotation process
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#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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## Bias, Risks, and Limitations
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_Unbabel__TowerBase-7B-v0.1 | [
"region:us"
] | 2024-01-13T18:52:49+00:00 | {"pretty_name": "Evaluation run of Unbabel/TowerBase-7B-v0.1", "dataset_summary": "Dataset automatically created during the evaluation run of model [Unbabel/TowerBase-7B-v0.1](https://huggingface.co/Unbabel/TowerBase-7B-v0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Unbabel__TowerBase-7B-v0.1\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T18:50:27.460863](https://huggingface.co/datasets/open-llm-leaderboard/details_Unbabel__TowerBase-7B-v0.1/blob/main/results_2024-01-13T18-50-27.460863.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.4376530652603909,\n \"acc_stderr\": 0.03443246082169724,\n \"acc_norm\": 0.4418549088967034,\n \"acc_norm_stderr\": 0.035215274463337505,\n \"mc1\": 0.23745410036719705,\n \"mc1_stderr\": 0.014896277441041843,\n \"mc2\": 0.3729251403943506,\n \"mc2_stderr\": 0.013360710032144346\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.48464163822525597,\n \"acc_stderr\": 0.014604496129394906,\n \"acc_norm\": 0.5102389078498294,\n \"acc_norm_stderr\": 0.014608326906285012\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5780720971917944,\n \"acc_stderr\": 0.004928578106026371,\n \"acc_norm\": 0.7768372834096794,\n \"acc_norm_stderr\": 0.0041551563175093375\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4148148148148148,\n \"acc_stderr\": 0.04256193767901407,\n \"acc_norm\": 0.4148148148148148,\n \"acc_norm_stderr\": 0.04256193767901407\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.39473684210526316,\n \"acc_stderr\": 0.039777499346220734,\n \"acc_norm\": 0.39473684210526316,\n \"acc_norm_stderr\": 0.039777499346220734\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.4226415094339623,\n \"acc_stderr\": 0.030402331445769537,\n \"acc_norm\": 0.4226415094339623,\n \"acc_norm_stderr\": 0.030402331445769537\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4305555555555556,\n \"acc_stderr\": 0.04140685639111503,\n \"acc_norm\": 0.4305555555555556,\n \"acc_norm_stderr\": 0.04140685639111503\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.4277456647398844,\n \"acc_stderr\": 0.037724468575180255,\n \"acc_norm\": 0.4277456647398844,\n \"acc_norm_stderr\": 0.037724468575180255\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.18627450980392157,\n \"acc_stderr\": 0.038739587141493524,\n \"acc_norm\": 0.18627450980392157,\n \"acc_norm_stderr\": 0.038739587141493524\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.37872340425531914,\n \"acc_stderr\": 0.03170995606040655,\n \"acc_norm\": 0.37872340425531914,\n \"acc_norm_stderr\": 0.03170995606040655\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.044346007015849245,\n \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.044346007015849245\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.42758620689655175,\n \"acc_stderr\": 0.04122737111370331,\n \"acc_norm\": 0.42758620689655175,\n \"acc_norm_stderr\": 0.04122737111370331\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.2804232804232804,\n \"acc_stderr\": 0.02313528797432563,\n \"acc_norm\": 0.2804232804232804,\n \"acc_norm_stderr\": 0.02313528797432563\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.23809523809523808,\n \"acc_stderr\": 0.038095238095238106,\n \"acc_norm\": 0.23809523809523808,\n \"acc_norm_stderr\": 0.038095238095238106\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.432258064516129,\n \"acc_stderr\": 0.02818173972001941,\n \"acc_norm\": 0.432258064516129,\n \"acc_norm_stderr\": 0.02818173972001941\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.2955665024630542,\n \"acc_stderr\": 0.032104944337514575,\n \"acc_norm\": 0.2955665024630542,\n \"acc_norm_stderr\": 0.032104944337514575\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.503030303030303,\n \"acc_stderr\": 0.03904272341431856,\n \"acc_norm\": 0.503030303030303,\n \"acc_norm_stderr\": 0.03904272341431856\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.4595959595959596,\n \"acc_stderr\": 0.035507024651313425,\n \"acc_norm\": 0.4595959595959596,\n \"acc_norm_stderr\": 0.035507024651313425\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.6113989637305699,\n \"acc_stderr\": 0.03517739796373131,\n \"acc_norm\": 0.6113989637305699,\n \"acc_norm_stderr\": 0.03517739796373131\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.43333333333333335,\n \"acc_stderr\": 0.025124653525885117,\n \"acc_norm\": 0.43333333333333335,\n \"acc_norm_stderr\": 0.025124653525885117\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.29259259259259257,\n \"acc_stderr\": 0.02773896963217609,\n \"acc_norm\": 0.29259259259259257,\n \"acc_norm_stderr\": 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["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T18-50-27.460863.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T18-50-27.460863.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_01_13T18_50_27.460863", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T18-50-27.460863.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T18-50-27.460863.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_01_13T18_50_27.460863", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T18-50-27.460863.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T18-50-27.460863.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_01_13T18_50_27.460863", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T18-50-27.460863.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T18-50-27.460863.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_13T18_50_27.460863", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T18-50-27.460863.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T18-50-27.460863.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_13T18_50_27.460863", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T18-50-27.460863.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T18-50-27.460863.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_13T18_50_27.460863", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T18-50-27.460863.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T18-50-27.460863.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T18_50_27.460863", "path": ["**/details_harness|winogrande|5_2024-01-13T18-50-27.460863.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T18-50-27.460863.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T18_50_27.460863", "path": ["results_2024-01-13T18-50-27.460863.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T18-50-27.460863.parquet"]}]}]} | 2024-01-13T18:53:09+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of Unbabel/TowerBase-7B-v0.1
Dataset automatically created during the evaluation run of model Unbabel/TowerBase-7B-v0.1 on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-13T18:50:27.460863(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of Unbabel/TowerBase-7B-v0.1\n\n\n\nDataset automatically created during the evaluation run of model Unbabel/TowerBase-7B-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T18:50:27.460863(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of Unbabel/TowerBase-7B-v0.1\n\n\n\nDataset automatically created during the evaluation run of model Unbabel/TowerBase-7B-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T18:50:27.460863(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
b47679c2c04734ea5423eec85c3583834173b4cd |
# Dataset Card for Evaluation run of aihub-app/zyte-1B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [aihub-app/zyte-1B](https://huggingface.co/aihub-app/zyte-1B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_aihub-app__zyte-1B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T18:52:20.951527](https://huggingface.co/datasets/open-llm-leaderboard/details_aihub-app__zyte-1B/blob/main/results_2024-01-13T18-52-20.951527.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
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"acc_stderr": 0.030560550793178157,
"acc_norm": 0.2546044559605402,
"acc_norm_stderr": 0.031312162600645795,
"mc1": 0.2717258261933905,
"mc1_stderr": 0.015572840452875828,
"mc2": 0.4214033627668609,
"mc2_stderr": 0.01468478270821933
},
"harness|arc:challenge|25": {
"acc": 0.34726962457337884,
"acc_stderr": 0.013913034529620434,
"acc_norm": 0.378839590443686,
"acc_norm_stderr": 0.014175915490000324
},
"harness|hellaswag|10": {
"acc": 0.4567815176259709,
"acc_stderr": 0.004971106265046556,
"acc_norm": 0.6137223660625374,
"acc_norm_stderr": 0.0048590041846946225
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.26,
"acc_stderr": 0.0440844002276808,
"acc_norm": 0.26,
"acc_norm_stderr": 0.0440844002276808
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.2962962962962963,
"acc_stderr": 0.03944624162501116,
"acc_norm": 0.2962962962962963,
"acc_norm_stderr": 0.03944624162501116
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.19736842105263158,
"acc_stderr": 0.03238981601699397,
"acc_norm": 0.19736842105263158,
"acc_norm_stderr": 0.03238981601699397
},
"harness|hendrycksTest-business_ethics|5": {
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"acc_stderr": 0.03861229196653695,
"acc_norm": 0.18,
"acc_norm_stderr": 0.03861229196653695
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.21509433962264152,
"acc_stderr": 0.025288394502891366,
"acc_norm": 0.21509433962264152,
"acc_norm_stderr": 0.025288394502891366
},
"harness|hendrycksTest-college_biology|5": {
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"acc_norm": 0.2569444444444444,
"acc_norm_stderr": 0.03653946969442099
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.27,
"acc_stderr": 0.0446196043338474,
"acc_norm": 0.27,
"acc_norm_stderr": 0.0446196043338474
},
"harness|hendrycksTest-college_mathematics|5": {
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"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-college_medicine|5": {
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},
"harness|hendrycksTest-college_physics|5": {
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},
"harness|hendrycksTest-computer_security|5": {
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},
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},
"harness|hendrycksTest-econometrics|5": {
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"acc_norm": 0.15789473684210525,
"acc_norm_stderr": 0.034302659784856984
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.2482758620689655,
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"acc_norm": 0.2482758620689655,
"acc_norm_stderr": 0.03600105692727772
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.23809523809523808,
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"acc_norm": 0.23809523809523808,
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},
"harness|hendrycksTest-formal_logic|5": {
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},
"harness|hendrycksTest-global_facts|5": {
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"harness|hendrycksTest-high_school_biology|5": {
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"harness|hendrycksTest-high_school_chemistry|5": {
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"harness|hendrycksTest-high_school_computer_science|5": {
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"harness|hendrycksTest-high_school_geography|5": {
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},
"harness|hendrycksTest-high_school_government_and_politics|5": {
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},
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"harness|hendrycksTest-high_school_microeconomics|5": {
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"harness|hendrycksTest-high_school_physics|5": {
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"harness|hendrycksTest-high_school_psychology|5": {
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"harness|hendrycksTest-high_school_us_history|5": {
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},
"harness|hendrycksTest-medical_genetics|5": {
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},
"harness|hendrycksTest-miscellaneous|5": {
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},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.2543352601156069,
"acc_stderr": 0.023445826276545526,
"acc_norm": 0.2543352601156069,
"acc_norm_stderr": 0.023445826276545526
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.26927374301675977,
"acc_stderr": 0.014835616582882578,
"acc_norm": 0.26927374301675977,
"acc_norm_stderr": 0.014835616582882578
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.24509803921568626,
"acc_stderr": 0.02463004897982476,
"acc_norm": 0.24509803921568626,
"acc_norm_stderr": 0.02463004897982476
},
"harness|hendrycksTest-philosophy|5": {
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"acc_stderr": 0.02492672322484554,
"acc_norm": 0.2604501607717042,
"acc_norm_stderr": 0.02492672322484554
},
"harness|hendrycksTest-prehistory|5": {
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"acc_stderr": 0.0242885336377261,
"acc_norm": 0.25617283950617287,
"acc_norm_stderr": 0.0242885336377261
},
"harness|hendrycksTest-professional_accounting|5": {
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"acc_norm": 0.24468085106382978,
"acc_norm_stderr": 0.025645553622266733
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"harness|hendrycksTest-professional_law|5": {
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"acc_norm": 0.2457627118644068,
"acc_norm_stderr": 0.010996156635142692
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"harness|hendrycksTest-professional_medicine|5": {
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"acc_norm": 0.24632352941176472,
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"harness|hendrycksTest-professional_psychology|5": {
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"acc_stderr": 0.017848089574913222,
"acc_norm": 0.2647058823529412,
"acc_norm_stderr": 0.017848089574913222
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.24545454545454545,
"acc_stderr": 0.041220665028782834,
"acc_norm": 0.24545454545454545,
"acc_norm_stderr": 0.041220665028782834
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.17142857142857143,
"acc_stderr": 0.024127463462650135,
"acc_norm": 0.17142857142857143,
"acc_norm_stderr": 0.024127463462650135
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.25870646766169153,
"acc_stderr": 0.030965903123573037,
"acc_norm": 0.25870646766169153,
"acc_norm_stderr": 0.030965903123573037
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.25,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.25,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-virology|5": {
"acc": 0.3313253012048193,
"acc_stderr": 0.036643147772880864,
"acc_norm": 0.3313253012048193,
"acc_norm_stderr": 0.036643147772880864
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.21052631578947367,
"acc_stderr": 0.031267817146631786,
"acc_norm": 0.21052631578947367,
"acc_norm_stderr": 0.031267817146631786
},
"harness|truthfulqa:mc|0": {
"mc1": 0.2717258261933905,
"mc1_stderr": 0.015572840452875828,
"mc2": 0.4214033627668609,
"mc2_stderr": 0.01468478270821933
},
"harness|winogrande|5": {
"acc": 0.6195737963693765,
"acc_stderr": 0.013644727908656831
},
"harness|gsm8k|5": {
"acc": 0.014404852160727824,
"acc_stderr": 0.0032820559171369795
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Annotation process
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#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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## Bias, Risks, and Limitations
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_aihub-app__zyte-1B | [
"region:us"
] | 2024-01-13T18:54:15+00:00 | {"pretty_name": "Evaluation run of aihub-app/zyte-1B", "dataset_summary": "Dataset automatically created during the evaluation run of model [aihub-app/zyte-1B](https://huggingface.co/aihub-app/zyte-1B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_aihub-app__zyte-1B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T18:52:20.951527](https://huggingface.co/datasets/open-llm-leaderboard/details_aihub-app__zyte-1B/blob/main/results_2024-01-13T18-52-20.951527.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.2535595202605755,\n \"acc_stderr\": 0.030560550793178157,\n \"acc_norm\": 0.2546044559605402,\n \"acc_norm_stderr\": 0.031312162600645795,\n \"mc1\": 0.2717258261933905,\n \"mc1_stderr\": 0.015572840452875828,\n \"mc2\": 0.4214033627668609,\n \"mc2_stderr\": 0.01468478270821933\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.34726962457337884,\n \"acc_stderr\": 0.013913034529620434,\n \"acc_norm\": 0.378839590443686,\n \"acc_norm_stderr\": 0.014175915490000324\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.4567815176259709,\n \"acc_stderr\": 0.004971106265046556,\n \"acc_norm\": 0.6137223660625374,\n \"acc_norm_stderr\": 0.0048590041846946225\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.26,\n \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.2962962962962963,\n \"acc_stderr\": 0.03944624162501116,\n \"acc_norm\": 0.2962962962962963,\n \"acc_norm_stderr\": 0.03944624162501116\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.19736842105263158,\n \"acc_stderr\": 0.03238981601699397,\n \"acc_norm\": 0.19736842105263158,\n \"acc_norm_stderr\": 0.03238981601699397\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.18,\n \"acc_stderr\": 0.03861229196653695,\n \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.03861229196653695\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.21509433962264152,\n \"acc_stderr\": 0.025288394502891366,\n \"acc_norm\": 0.21509433962264152,\n \"acc_norm_stderr\": 0.025288394502891366\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2569444444444444,\n \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.2569444444444444,\n \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.1791907514450867,\n \"acc_stderr\": 0.02924251305906328,\n \"acc_norm\": 0.1791907514450867,\n \"acc_norm_stderr\": 0.02924251305906328\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.2549019607843137,\n \"acc_stderr\": 0.043364327079931785,\n \"acc_norm\": 0.2549019607843137,\n \"acc_norm_stderr\": 0.043364327079931785\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.2723404255319149,\n \"acc_stderr\": 0.0291012906983867,\n \"acc_norm\": 0.2723404255319149,\n \"acc_norm_stderr\": 0.0291012906983867\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.15789473684210525,\n \"acc_stderr\": 0.034302659784856984,\n \"acc_norm\": 0.15789473684210525,\n \"acc_norm_stderr\": 0.034302659784856984\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.2482758620689655,\n \"acc_stderr\": 0.03600105692727772,\n \"acc_norm\": 0.2482758620689655,\n \"acc_norm_stderr\": 0.03600105692727772\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.23809523809523808,\n \"acc_stderr\": 0.021935878081184756,\n \"acc_norm\": 0.23809523809523808,\n \"acc_norm_stderr\": 0.021935878081184756\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.16666666666666666,\n \"acc_stderr\": 0.03333333333333338,\n \"acc_norm\": 0.16666666666666666,\n \"acc_norm_stderr\": 0.03333333333333338\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.19,\n \"acc_stderr\": 0.039427724440366255,\n \"acc_norm\": 0.19,\n \"acc_norm_stderr\": 0.039427724440366255\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.1967741935483871,\n \"acc_stderr\": 0.022616409420742018,\n \"acc_norm\": 0.1967741935483871,\n \"acc_norm_stderr\": 0.022616409420742018\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.20689655172413793,\n \"acc_stderr\": 0.028501378167893946,\n \"acc_norm\": 0.20689655172413793,\n \"acc_norm_stderr\": 0.028501378167893946\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.22424242424242424,\n \"acc_stderr\": 0.03256866661681102,\n \"acc_norm\": 0.22424242424242424,\n \"acc_norm_stderr\": 0.03256866661681102\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.22727272727272727,\n \"acc_stderr\": 0.029857515673386407,\n \"acc_norm\": 0.22727272727272727,\n \"acc_norm_stderr\": 0.029857515673386407\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.20725388601036268,\n \"acc_stderr\": 0.029252823291803624,\n \"acc_norm\": 0.20725388601036268,\n \"acc_norm_stderr\": 0.029252823291803624\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.2512820512820513,\n \"acc_stderr\": 0.021992016662370547,\n \"acc_norm\": 0.2512820512820513,\n \"acc_norm_stderr\": 0.021992016662370547\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.21851851851851853,\n \"acc_stderr\": 0.025195752251823796,\n \"acc_norm\": 0.21851851851851853,\n \"acc_norm_stderr\": 0.025195752251823796\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.23109243697478993,\n \"acc_stderr\": 0.027381406927868952,\n \"acc_norm\": 0.23109243697478993,\n \"acc_norm_stderr\": 0.027381406927868952\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.25165562913907286,\n \"acc_stderr\": 0.035433042343899844,\n \"acc_norm\": 0.25165562913907286,\n \"acc_norm_stderr\": 0.035433042343899844\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.24036697247706423,\n \"acc_stderr\": 0.01832060732096407,\n \"acc_norm\": 0.24036697247706423,\n \"acc_norm_stderr\": 0.01832060732096407\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.33796296296296297,\n \"acc_stderr\": 0.03225941352631295,\n \"acc_norm\": 0.33796296296296297,\n \"acc_norm_stderr\": 0.03225941352631295\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.029331162294251728,\n \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.029331162294251728\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.2742616033755274,\n \"acc_stderr\": 0.029041333510598028,\n \"acc_norm\": 0.2742616033755274,\n \"acc_norm_stderr\": 0.029041333510598028\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.3273542600896861,\n \"acc_stderr\": 0.031493846709941306,\n \"acc_norm\": 0.3273542600896861,\n \"acc_norm_stderr\": 0.031493846709941306\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.21374045801526717,\n \"acc_stderr\": 0.0359546161177469,\n \"acc_norm\": 0.21374045801526717,\n \"acc_norm_stderr\": 0.0359546161177469\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.256198347107438,\n \"acc_stderr\": 0.03984979653302871,\n \"acc_norm\": 0.256198347107438,\n \"acc_norm_stderr\": 0.03984979653302871\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.21296296296296297,\n \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.21296296296296297,\n \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.24539877300613497,\n \"acc_stderr\": 0.03380939813943354,\n \"acc_norm\": 0.24539877300613497,\n \"acc_norm_stderr\": 0.03380939813943354\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.29464285714285715,\n \"acc_stderr\": 0.043270409325787296,\n \"acc_norm\": 0.29464285714285715,\n \"acc_norm_stderr\": 0.043270409325787296\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.1941747572815534,\n \"acc_stderr\": 0.03916667762822584,\n \"acc_norm\": 0.1941747572815534,\n \"acc_norm_stderr\": 0.03916667762822584\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.21794871794871795,\n \"acc_stderr\": 0.027046857630716677,\n \"acc_norm\": 0.21794871794871795,\n \"acc_norm_stderr\": 0.027046857630716677\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.2962962962962963,\n \"acc_stderr\": 0.016328814422102055,\n \"acc_norm\": 0.2962962962962963,\n \"acc_norm_stderr\": 0.016328814422102055\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.2543352601156069,\n \"acc_stderr\": 0.023445826276545526,\n \"acc_norm\": 0.2543352601156069,\n \"acc_norm_stderr\": 0.023445826276545526\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.26927374301675977,\n \"acc_stderr\": 0.014835616582882578,\n \"acc_norm\": 0.26927374301675977,\n \"acc_norm_stderr\": 0.014835616582882578\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.24509803921568626,\n \"acc_stderr\": 0.02463004897982476,\n \"acc_norm\": 0.24509803921568626,\n \"acc_norm_stderr\": 0.02463004897982476\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2604501607717042,\n 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#region-us
|
# Dataset Card for Evaluation run of aihub-app/zyte-1B
Dataset automatically created during the evaluation run of model aihub-app/zyte-1B on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-13T18:52:20.951527(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of aihub-app/zyte-1B\n\n\n\nDataset automatically created during the evaluation run of model aihub-app/zyte-1B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T18:52:20.951527(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of aihub-app/zyte-1B\n\n\n\nDataset automatically created during the evaluation run of model aihub-app/zyte-1B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T18:52:20.951527(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
b54fd84b7d613994a9d7383237910c2b95204c3b |
# Dataset Card for Evaluation run of rombodawg/Open_Gpt4_8x7B_v0.2
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [rombodawg/Open_Gpt4_8x7B_v0.2](https://huggingface.co/rombodawg/Open_Gpt4_8x7B_v0.2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_rombodawg__Open_Gpt4_8x7B_v0.2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T18:56:10.033721](https://huggingface.co/datasets/open-llm-leaderboard/details_rombodawg__Open_Gpt4_8x7B_v0.2/blob/main/results_2024-01-13T18-56-10.033721.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.7188157275221039,
"acc_stderr": 0.030029707306740233,
"acc_norm": 0.7225114431475408,
"acc_norm_stderr": 0.03061684137993921,
"mc1": 0.5605875152998776,
"mc1_stderr": 0.017374520482513707,
"mc2": 0.7191590734021742,
"mc2_stderr": 0.014814881257041205
},
"harness|arc:challenge|25": {
"acc": 0.6646757679180887,
"acc_stderr": 0.01379618294778556,
"acc_norm": 0.6868600682593856,
"acc_norm_stderr": 0.013552671543623496
},
"harness|hellaswag|10": {
"acc": 0.6761601274646485,
"acc_stderr": 0.0046698341309770785,
"acc_norm": 0.8615813582951604,
"acc_norm_stderr": 0.0034463307489637123
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.4,
"acc_stderr": 0.049236596391733084,
"acc_norm": 0.4,
"acc_norm_stderr": 0.049236596391733084
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.7407407407407407,
"acc_stderr": 0.03785714465066653,
"acc_norm": 0.7407407407407407,
"acc_norm_stderr": 0.03785714465066653
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.8223684210526315,
"acc_stderr": 0.031103182383123377,
"acc_norm": 0.8223684210526315,
"acc_norm_stderr": 0.031103182383123377
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.73,
"acc_stderr": 0.0446196043338474,
"acc_norm": 0.73,
"acc_norm_stderr": 0.0446196043338474
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7962264150943397,
"acc_stderr": 0.024790784501775406,
"acc_norm": 0.7962264150943397,
"acc_norm_stderr": 0.024790784501775406
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.8402777777777778,
"acc_stderr": 0.030635578972093288,
"acc_norm": 0.8402777777777778,
"acc_norm_stderr": 0.030635578972093288
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.53,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.53,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.59,
"acc_stderr": 0.04943110704237102,
"acc_norm": 0.59,
"acc_norm_stderr": 0.04943110704237102
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.7514450867052023,
"acc_stderr": 0.03295304696818318,
"acc_norm": 0.7514450867052023,
"acc_norm_stderr": 0.03295304696818318
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.5196078431372549,
"acc_stderr": 0.04971358884367405,
"acc_norm": 0.5196078431372549,
"acc_norm_stderr": 0.04971358884367405
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.79,
"acc_stderr": 0.04093601807403326,
"acc_norm": 0.79,
"acc_norm_stderr": 0.04093601807403326
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.723404255319149,
"acc_stderr": 0.02924188386962882,
"acc_norm": 0.723404255319149,
"acc_norm_stderr": 0.02924188386962882
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.631578947368421,
"acc_stderr": 0.04537815354939391,
"acc_norm": 0.631578947368421,
"acc_norm_stderr": 0.04537815354939391
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.6689655172413793,
"acc_stderr": 0.03921545312467122,
"acc_norm": 0.6689655172413793,
"acc_norm_stderr": 0.03921545312467122
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.5317460317460317,
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"acc_norm": 0.5317460317460317,
"acc_norm_stderr": 0.0256993528321318
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.5317460317460317,
"acc_stderr": 0.04463112720677173,
"acc_norm": 0.5317460317460317,
"acc_norm_stderr": 0.04463112720677173
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.41,
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"acc_norm": 0.41,
"acc_norm_stderr": 0.049431107042371025
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.864516129032258,
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"acc_norm": 0.864516129032258,
"acc_norm_stderr": 0.01946933458648693
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.6305418719211823,
"acc_stderr": 0.03395970381998574,
"acc_norm": 0.6305418719211823,
"acc_norm_stderr": 0.03395970381998574
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.73,
"acc_stderr": 0.044619604333847394,
"acc_norm": 0.73,
"acc_norm_stderr": 0.044619604333847394
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.8121212121212121,
"acc_stderr": 0.03050193405942914,
"acc_norm": 0.8121212121212121,
"acc_norm_stderr": 0.03050193405942914
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8686868686868687,
"acc_stderr": 0.02406315641682253,
"acc_norm": 0.8686868686868687,
"acc_norm_stderr": 0.02406315641682253
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9378238341968912,
"acc_stderr": 0.017426974154240524,
"acc_norm": 0.9378238341968912,
"acc_norm_stderr": 0.017426974154240524
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.7205128205128205,
"acc_stderr": 0.022752388839776823,
"acc_norm": 0.7205128205128205,
"acc_norm_stderr": 0.022752388839776823
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.36666666666666664,
"acc_stderr": 0.02938162072646507,
"acc_norm": 0.36666666666666664,
"acc_norm_stderr": 0.02938162072646507
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.8109243697478992,
"acc_stderr": 0.025435119438105364,
"acc_norm": 0.8109243697478992,
"acc_norm_stderr": 0.025435119438105364
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.47019867549668876,
"acc_stderr": 0.040752249922169775,
"acc_norm": 0.47019867549668876,
"acc_norm_stderr": 0.040752249922169775
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8990825688073395,
"acc_stderr": 0.012914673545364432,
"acc_norm": 0.8990825688073395,
"acc_norm_stderr": 0.012914673545364432
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5972222222222222,
"acc_stderr": 0.03344887382997865,
"acc_norm": 0.5972222222222222,
"acc_norm_stderr": 0.03344887382997865
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8774509803921569,
"acc_stderr": 0.023015389732458265,
"acc_norm": 0.8774509803921569,
"acc_norm_stderr": 0.023015389732458265
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8776371308016878,
"acc_stderr": 0.021331741829746786,
"acc_norm": 0.8776371308016878,
"acc_norm_stderr": 0.021331741829746786
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.7443946188340808,
"acc_stderr": 0.029275891003969927,
"acc_norm": 0.7443946188340808,
"acc_norm_stderr": 0.029275891003969927
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7938931297709924,
"acc_stderr": 0.03547771004159464,
"acc_norm": 0.7938931297709924,
"acc_norm_stderr": 0.03547771004159464
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8512396694214877,
"acc_stderr": 0.03248470083807194,
"acc_norm": 0.8512396694214877,
"acc_norm_stderr": 0.03248470083807194
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.8796296296296297,
"acc_stderr": 0.031457038543062504,
"acc_norm": 0.8796296296296297,
"acc_norm_stderr": 0.031457038543062504
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7975460122699386,
"acc_stderr": 0.03157065078911899,
"acc_norm": 0.7975460122699386,
"acc_norm_stderr": 0.03157065078911899
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.6160714285714286,
"acc_stderr": 0.046161430750285455,
"acc_norm": 0.6160714285714286,
"acc_norm_stderr": 0.046161430750285455
},
"harness|hendrycksTest-management|5": {
"acc": 0.8155339805825242,
"acc_stderr": 0.03840423627288276,
"acc_norm": 0.8155339805825242,
"acc_norm_stderr": 0.03840423627288276
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8974358974358975,
"acc_stderr": 0.019875655027867457,
"acc_norm": 0.8974358974358975,
"acc_norm_stderr": 0.019875655027867457
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.79,
"acc_stderr": 0.040936018074033256,
"acc_norm": 0.79,
"acc_norm_stderr": 0.040936018074033256
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8812260536398467,
"acc_stderr": 0.011569134791715655,
"acc_norm": 0.8812260536398467,
"acc_norm_stderr": 0.011569134791715655
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7976878612716763,
"acc_stderr": 0.021628077380196124,
"acc_norm": 0.7976878612716763,
"acc_norm_stderr": 0.021628077380196124
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.5687150837988827,
"acc_stderr": 0.01656382939904771,
"acc_norm": 0.5687150837988827,
"acc_norm_stderr": 0.01656382939904771
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7679738562091504,
"acc_stderr": 0.024170840879340866,
"acc_norm": 0.7679738562091504,
"acc_norm_stderr": 0.024170840879340866
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7942122186495176,
"acc_stderr": 0.022961339906764244,
"acc_norm": 0.7942122186495176,
"acc_norm_stderr": 0.022961339906764244
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.8518518518518519,
"acc_stderr": 0.019766459563597252,
"acc_norm": 0.8518518518518519,
"acc_norm_stderr": 0.019766459563597252
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.5319148936170213,
"acc_stderr": 0.02976667507587387,
"acc_norm": 0.5319148936170213,
"acc_norm_stderr": 0.02976667507587387
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.5436766623207301,
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"acc_norm": 0.5436766623207301,
"acc_norm_stderr": 0.012721420501462547
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.7904411764705882,
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"acc_norm": 0.7904411764705882,
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},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.7696078431372549,
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"acc_norm": 0.7696078431372549,
"acc_norm_stderr": 0.017035229258034038
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.7272727272727273,
"acc_stderr": 0.04265792110940588,
"acc_norm": 0.7272727272727273,
"acc_norm_stderr": 0.04265792110940588
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.8040816326530612,
"acc_stderr": 0.025409301953225678,
"acc_norm": 0.8040816326530612,
"acc_norm_stderr": 0.025409301953225678
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.900497512437811,
"acc_stderr": 0.02116621630465939,
"acc_norm": 0.900497512437811,
"acc_norm_stderr": 0.02116621630465939
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.92,
"acc_stderr": 0.0272659924344291,
"acc_norm": 0.92,
"acc_norm_stderr": 0.0272659924344291
},
"harness|hendrycksTest-virology|5": {
"acc": 0.536144578313253,
"acc_stderr": 0.038823108508905954,
"acc_norm": 0.536144578313253,
"acc_norm_stderr": 0.038823108508905954
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8771929824561403,
"acc_stderr": 0.02517298435015574,
"acc_norm": 0.8771929824561403,
"acc_norm_stderr": 0.02517298435015574
},
"harness|truthfulqa:mc|0": {
"mc1": 0.5605875152998776,
"mc1_stderr": 0.017374520482513707,
"mc2": 0.7191590734021742,
"mc2_stderr": 0.014814881257041205
},
"harness|winogrande|5": {
"acc": 0.8358326756116812,
"acc_stderr": 0.0104108497752228
},
"harness|gsm8k|5": {
"acc": 0.5913570887035633,
"acc_stderr": 0.013540639733342429
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
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[More Information Needed] | open-llm-leaderboard/details_rombodawg__Open_Gpt4_8x7B_v0.2 | [
"region:us"
] | 2024-01-13T18:58:32+00:00 | {"pretty_name": "Evaluation run of rombodawg/Open_Gpt4_8x7B_v0.2", "dataset_summary": "Dataset automatically created during the evaluation run of model [rombodawg/Open_Gpt4_8x7B_v0.2](https://huggingface.co/rombodawg/Open_Gpt4_8x7B_v0.2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_rombodawg__Open_Gpt4_8x7B_v0.2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T18:56:10.033721](https://huggingface.co/datasets/open-llm-leaderboard/details_rombodawg__Open_Gpt4_8x7B_v0.2/blob/main/results_2024-01-13T18-56-10.033721.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.7188157275221039,\n \"acc_stderr\": 0.030029707306740233,\n \"acc_norm\": 0.7225114431475408,\n \"acc_norm_stderr\": 0.03061684137993921,\n \"mc1\": 0.5605875152998776,\n \"mc1_stderr\": 0.017374520482513707,\n \"mc2\": 0.7191590734021742,\n \"mc2_stderr\": 0.014814881257041205\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6646757679180887,\n \"acc_stderr\": 0.01379618294778556,\n \"acc_norm\": 0.6868600682593856,\n \"acc_norm_stderr\": 0.013552671543623496\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6761601274646485,\n \"acc_stderr\": 0.0046698341309770785,\n \"acc_norm\": 0.8615813582951604,\n \"acc_norm_stderr\": 0.0034463307489637123\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.7407407407407407,\n \"acc_stderr\": 0.03785714465066653,\n \"acc_norm\": 0.7407407407407407,\n \"acc_norm_stderr\": 0.03785714465066653\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.8223684210526315,\n \"acc_stderr\": 0.031103182383123377,\n \"acc_norm\": 0.8223684210526315,\n \"acc_norm_stderr\": 0.031103182383123377\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.73,\n \"acc_stderr\": 0.0446196043338474,\n \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.7962264150943397,\n \"acc_stderr\": 0.024790784501775406,\n \"acc_norm\": 0.7962264150943397,\n \"acc_norm_stderr\": 0.024790784501775406\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8402777777777778,\n \"acc_stderr\": 0.030635578972093288,\n \"acc_norm\": 0.8402777777777778,\n \"acc_norm_stderr\": 0.030635578972093288\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.59,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7514450867052023,\n \"acc_stderr\": 0.03295304696818318,\n \"acc_norm\": 0.7514450867052023,\n \"acc_norm_stderr\": 0.03295304696818318\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.5196078431372549,\n \"acc_stderr\": 0.04971358884367405,\n \"acc_norm\": 0.5196078431372549,\n \"acc_norm_stderr\": 0.04971358884367405\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.79,\n \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.04093601807403326\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.723404255319149,\n \"acc_stderr\": 0.02924188386962882,\n \"acc_norm\": 0.723404255319149,\n \"acc_norm_stderr\": 0.02924188386962882\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.631578947368421,\n \"acc_stderr\": 0.04537815354939391,\n \"acc_norm\": 0.631578947368421,\n \"acc_norm_stderr\": 0.04537815354939391\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.6689655172413793,\n \"acc_stderr\": 0.03921545312467122,\n \"acc_norm\": 0.6689655172413793,\n \"acc_norm_stderr\": 0.03921545312467122\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.5317460317460317,\n \"acc_stderr\": 0.0256993528321318,\n \"acc_norm\": 0.5317460317460317,\n \"acc_norm_stderr\": 0.0256993528321318\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5317460317460317,\n \"acc_stderr\": 0.04463112720677173,\n \"acc_norm\": 0.5317460317460317,\n \"acc_norm_stderr\": 0.04463112720677173\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.864516129032258,\n \"acc_stderr\": 0.01946933458648693,\n \"acc_norm\": 0.864516129032258,\n \"acc_norm_stderr\": 0.01946933458648693\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.6305418719211823,\n \"acc_stderr\": 0.03395970381998574,\n \"acc_norm\": 0.6305418719211823,\n \"acc_norm_stderr\": 0.03395970381998574\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.8121212121212121,\n \"acc_stderr\": 0.03050193405942914,\n \"acc_norm\": 0.8121212121212121,\n \"acc_norm_stderr\": 0.03050193405942914\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.8686868686868687,\n \"acc_stderr\": 0.02406315641682253,\n \"acc_norm\": 0.8686868686868687,\n \"acc_norm_stderr\": 0.02406315641682253\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.9378238341968912,\n \"acc_stderr\": 0.017426974154240524,\n \"acc_norm\": 0.9378238341968912,\n \"acc_norm_stderr\": 0.017426974154240524\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.7205128205128205,\n \"acc_stderr\": 0.022752388839776823,\n \"acc_norm\": 0.7205128205128205,\n \"acc_norm_stderr\": 0.022752388839776823\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.36666666666666664,\n \"acc_stderr\": 0.02938162072646507,\n \"acc_norm\": 0.36666666666666664,\n \"acc_norm_stderr\": 0.02938162072646507\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.8109243697478992,\n \"acc_stderr\": 0.025435119438105364,\n \"acc_norm\": 0.8109243697478992,\n \"acc_norm_stderr\": 0.025435119438105364\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.47019867549668876,\n \"acc_stderr\": 0.040752249922169775,\n \"acc_norm\": 0.47019867549668876,\n \"acc_norm_stderr\": 0.040752249922169775\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8990825688073395,\n \"acc_stderr\": 0.012914673545364432,\n \"acc_norm\": 0.8990825688073395,\n \"acc_norm_stderr\": 0.012914673545364432\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5972222222222222,\n \"acc_stderr\": 0.03344887382997865,\n \"acc_norm\": 0.5972222222222222,\n \"acc_norm_stderr\": 0.03344887382997865\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8774509803921569,\n \"acc_stderr\": 0.023015389732458265,\n \"acc_norm\": 0.8774509803921569,\n \"acc_norm_stderr\": 0.023015389732458265\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.8776371308016878,\n \"acc_stderr\": 0.021331741829746786,\n \"acc_norm\": 0.8776371308016878,\n \"acc_norm_stderr\": 0.021331741829746786\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7443946188340808,\n \"acc_stderr\": 0.029275891003969927,\n \"acc_norm\": 0.7443946188340808,\n \"acc_norm_stderr\": 0.029275891003969927\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.7938931297709924,\n \"acc_stderr\": 0.03547771004159464,\n \"acc_norm\": 0.7938931297709924,\n \"acc_norm_stderr\": 0.03547771004159464\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.8512396694214877,\n \"acc_stderr\": 0.03248470083807194,\n \"acc_norm\": 0.8512396694214877,\n \"acc_norm_stderr\": 0.03248470083807194\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8796296296296297,\n \"acc_stderr\": 0.031457038543062504,\n \"acc_norm\": 0.8796296296296297,\n \"acc_norm_stderr\": 0.031457038543062504\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7975460122699386,\n \"acc_stderr\": 0.03157065078911899,\n \"acc_norm\": 0.7975460122699386,\n \"acc_norm_stderr\": 0.03157065078911899\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.6160714285714286,\n \"acc_stderr\": 0.046161430750285455,\n \"acc_norm\": 0.6160714285714286,\n \"acc_norm_stderr\": 0.046161430750285455\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.8155339805825242,\n \"acc_stderr\": 0.03840423627288276,\n \"acc_norm\": 0.8155339805825242,\n \"acc_norm_stderr\": 0.03840423627288276\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8974358974358975,\n \"acc_stderr\": 0.019875655027867457,\n \"acc_norm\": 0.8974358974358975,\n \"acc_norm_stderr\": 0.019875655027867457\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8812260536398467,\n \"acc_stderr\": 0.011569134791715655,\n \"acc_norm\": 0.8812260536398467,\n \"acc_norm_stderr\": 0.011569134791715655\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7976878612716763,\n \"acc_stderr\": 0.021628077380196124,\n \"acc_norm\": 0.7976878612716763,\n \"acc_norm_stderr\": 0.021628077380196124\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.5687150837988827,\n \"acc_stderr\": 0.01656382939904771,\n \"acc_norm\": 0.5687150837988827,\n \"acc_norm_stderr\": 0.01656382939904771\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.7679738562091504,\n \"acc_stderr\": 0.024170840879340866,\n \"acc_norm\": 0.7679738562091504,\n \"acc_norm_stderr\": 0.024170840879340866\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7942122186495176,\n \"acc_stderr\": 0.022961339906764244,\n \"acc_norm\": 0.7942122186495176,\n \"acc_norm_stderr\": 0.022961339906764244\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.8518518518518519,\n \"acc_stderr\": 0.019766459563597252,\n \"acc_norm\": 0.8518518518518519,\n \"acc_norm_stderr\": 0.019766459563597252\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.5319148936170213,\n \"acc_stderr\": 0.02976667507587387,\n \"acc_norm\": 0.5319148936170213,\n \"acc_norm_stderr\": 0.02976667507587387\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5436766623207301,\n \"acc_stderr\": 0.012721420501462547,\n \"acc_norm\": 0.5436766623207301,\n \"acc_norm_stderr\": 0.012721420501462547\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.7904411764705882,\n \"acc_stderr\": 0.02472311040767708,\n \"acc_norm\": 0.7904411764705882,\n \"acc_norm_stderr\": 0.02472311040767708\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.7696078431372549,\n \"acc_stderr\": 0.017035229258034038,\n \"acc_norm\": 0.7696078431372549,\n \"acc_norm_stderr\": 0.017035229258034038\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7272727272727273,\n \"acc_stderr\": 0.04265792110940588,\n \"acc_norm\": 0.7272727272727273,\n \"acc_norm_stderr\": 0.04265792110940588\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.8040816326530612,\n \"acc_stderr\": 0.025409301953225678,\n \"acc_norm\": 0.8040816326530612,\n \"acc_norm_stderr\": 0.025409301953225678\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.900497512437811,\n \"acc_stderr\": 0.02116621630465939,\n \"acc_norm\": 0.900497512437811,\n \"acc_norm_stderr\": 0.02116621630465939\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.92,\n \"acc_stderr\": 0.0272659924344291,\n \"acc_norm\": 0.92,\n \"acc_norm_stderr\": 0.0272659924344291\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.536144578313253,\n \"acc_stderr\": 0.038823108508905954,\n \"acc_norm\": 0.536144578313253,\n \"acc_norm_stderr\": 0.038823108508905954\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.8771929824561403,\n \"acc_stderr\": 0.02517298435015574,\n \"acc_norm\": 0.8771929824561403,\n \"acc_norm_stderr\": 0.02517298435015574\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5605875152998776,\n \"mc1_stderr\": 0.017374520482513707,\n \"mc2\": 0.7191590734021742,\n \"mc2_stderr\": 0.014814881257041205\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.8358326756116812,\n \"acc_stderr\": 0.0104108497752228\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5913570887035633,\n \"acc_stderr\": 0.013540639733342429\n }\n}\n```", "repo_url": "https://huggingface.co/rombodawg/Open_Gpt4_8x7B_v0.2", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2024_01_13T18_56_10.033721", "path": ["**/details_harness|arc:challenge|25_2024-01-13T18-56-10.033721.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-01-13T18-56-10.033721.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_01_13T18_56_10.033721", "path": ["**/details_harness|gsm8k|5_2024-01-13T18-56-10.033721.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-01-13T18-56-10.033721.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_01_13T18_56_10.033721", "path": ["**/details_harness|hellaswag|10_2024-01-13T18-56-10.033721.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-01-13T18-56-10.033721.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_01_13T18_56_10.033721", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T18-56-10.033721.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-01-13T18-56-10.033721.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-01-13T18-56-10.033721.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T18-56-10.033721.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T18-56-10.033721.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-01-13T18-56-10.033721.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T18-56-10.033721.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T18-56-10.033721.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T18-56-10.033721.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T18-56-10.033721.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-01-13T18-56-10.033721.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-01-13T18-56-10.033721.parquet", 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"path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-13T18-56-10.033721.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2024_01_13T18_56_10.033721", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T18-56-10.033721.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T18-56-10.033721.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2024_01_13T18_56_10.033721", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T18-56-10.033721.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T18-56-10.033721.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2024_01_13T18_56_10.033721", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T18-56-10.033721.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T18-56-10.033721.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2024_01_13T18_56_10.033721", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T18-56-10.033721.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T18-56-10.033721.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2024_01_13T18_56_10.033721", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T18-56-10.033721.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T18-56-10.033721.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2024_01_13T18_56_10.033721", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T18-56-10.033721.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T18-56-10.033721.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2024_01_13T18_56_10.033721", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T18-56-10.033721.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T18-56-10.033721.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2024_01_13T18_56_10.033721", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T18-56-10.033721.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T18-56-10.033721.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2024_01_13T18_56_10.033721", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T18-56-10.033721.parquet"]}, {"split": 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["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T18-56-10.033721.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T18-56-10.033721.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_13T18_56_10.033721", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T18-56-10.033721.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T18-56-10.033721.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_13T18_56_10.033721", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T18-56-10.033721.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T18-56-10.033721.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_13T18_56_10.033721", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T18-56-10.033721.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T18-56-10.033721.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T18_56_10.033721", "path": ["**/details_harness|winogrande|5_2024-01-13T18-56-10.033721.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T18-56-10.033721.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T18_56_10.033721", "path": ["results_2024-01-13T18-56-10.033721.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T18-56-10.033721.parquet"]}]}]} | 2024-01-13T18:58:52+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of rombodawg/Open_Gpt4_8x7B_v0.2
Dataset automatically created during the evaluation run of model rombodawg/Open_Gpt4_8x7B_v0.2 on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-13T18:56:10.033721(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of rombodawg/Open_Gpt4_8x7B_v0.2\n\n\n\nDataset automatically created during the evaluation run of model rombodawg/Open_Gpt4_8x7B_v0.2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T18:56:10.033721(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of rombodawg/Open_Gpt4_8x7B_v0.2\n\n\n\nDataset automatically created during the evaluation run of model rombodawg/Open_Gpt4_8x7B_v0.2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T18:56:10.033721(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
ac604b735bafa8f3c1b7ab602daf7de7fc2fb7ee |
# Dataset Card for Evaluation run of udkai/Turdus
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [udkai/Turdus](https://huggingface.co/udkai/Turdus) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_udkai__Turdus",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T18:57:41.292260](https://huggingface.co/datasets/open-llm-leaderboard/details_udkai__Turdus/blob/main/results_2024-01-13T18-57-41.292260.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6516181926648874,
"acc_stderr": 0.0321728872347043,
"acc_norm": 0.650729026842337,
"acc_norm_stderr": 0.03285551278950848,
"mc1": 0.5471236230110159,
"mc1_stderr": 0.01742558984831402,
"mc2": 0.6711400532546105,
"mc2_stderr": 0.015451181249566945
},
"harness|arc:challenge|25": {
"acc": 0.7107508532423208,
"acc_stderr": 0.013250012579393443,
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}
```
## Dataset Details
### Dataset Description
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## Dataset Structure
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## Dataset Creation
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### Source Data
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#### Data Collection and Processing
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[More Information Needed] | open-llm-leaderboard/details_udkai__Turdus | [
"region:us"
] | 2024-01-13T19:00:00+00:00 | {"pretty_name": "Evaluation run of udkai/Turdus", "dataset_summary": "Dataset automatically created during the evaluation run of model [udkai/Turdus](https://huggingface.co/udkai/Turdus) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_udkai__Turdus\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T18:57:41.292260](https://huggingface.co/datasets/open-llm-leaderboard/details_udkai__Turdus/blob/main/results_2024-01-13T18-57-41.292260.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6516181926648874,\n \"acc_stderr\": 0.0321728872347043,\n \"acc_norm\": 0.650729026842337,\n \"acc_norm_stderr\": 0.03285551278950848,\n \"mc1\": 0.5471236230110159,\n \"mc1_stderr\": 0.01742558984831402,\n \"mc2\": 0.6711400532546105,\n \"mc2_stderr\": 0.015451181249566945\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.7107508532423208,\n \"acc_stderr\": 0.013250012579393443,\n \"acc_norm\": 0.7337883959044369,\n \"acc_norm_stderr\": 0.012915774781523197\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7206731726747659,\n \"acc_stderr\": 0.004477514681328155,\n \"acc_norm\": 0.8855805616411073,\n \"acc_norm_stderr\": 0.0031766945645110784\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 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["**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T18-57-41.292260.parquet"]}]}, {"config_name": "harness_hendrycksTest_international_law_5", "data_files": [{"split": "2024_01_13T18_57_41.292260", "path": ["**/details_harness|hendrycksTest-international_law|5_2024-01-13T18-57-41.292260.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-international_law|5_2024-01-13T18-57-41.292260.parquet"]}]}, {"config_name": "harness_hendrycksTest_jurisprudence_5", "data_files": [{"split": "2024_01_13T18_57_41.292260", "path": ["**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T18-57-41.292260.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T18-57-41.292260.parquet"]}]}, {"config_name": "harness_hendrycksTest_logical_fallacies_5", "data_files": [{"split": "2024_01_13T18_57_41.292260", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T18-57-41.292260.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T18-57-41.292260.parquet"]}]}, {"config_name": "harness_hendrycksTest_machine_learning_5", "data_files": [{"split": "2024_01_13T18_57_41.292260", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T18-57-41.292260.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T18-57-41.292260.parquet"]}]}, {"config_name": "harness_hendrycksTest_management_5", "data_files": [{"split": "2024_01_13T18_57_41.292260", "path": ["**/details_harness|hendrycksTest-management|5_2024-01-13T18-57-41.292260.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-management|5_2024-01-13T18-57-41.292260.parquet"]}]}, {"config_name": "harness_hendrycksTest_marketing_5", "data_files": [{"split": "2024_01_13T18_57_41.292260", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-13T18-57-41.292260.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-13T18-57-41.292260.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2024_01_13T18_57_41.292260", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T18-57-41.292260.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T18-57-41.292260.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2024_01_13T18_57_41.292260", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T18-57-41.292260.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T18-57-41.292260.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2024_01_13T18_57_41.292260", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T18-57-41.292260.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T18-57-41.292260.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2024_01_13T18_57_41.292260", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T18-57-41.292260.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T18-57-41.292260.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2024_01_13T18_57_41.292260", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T18-57-41.292260.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T18-57-41.292260.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2024_01_13T18_57_41.292260", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T18-57-41.292260.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T18-57-41.292260.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2024_01_13T18_57_41.292260", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T18-57-41.292260.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T18-57-41.292260.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2024_01_13T18_57_41.292260", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T18-57-41.292260.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T18-57-41.292260.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2024_01_13T18_57_41.292260", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T18-57-41.292260.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T18-57-41.292260.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_01_13T18_57_41.292260", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T18-57-41.292260.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T18-57-41.292260.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_01_13T18_57_41.292260", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T18-57-41.292260.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T18-57-41.292260.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_01_13T18_57_41.292260", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T18-57-41.292260.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T18-57-41.292260.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_01_13T18_57_41.292260", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T18-57-41.292260.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T18-57-41.292260.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_01_13T18_57_41.292260", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T18-57-41.292260.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T18-57-41.292260.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_01_13T18_57_41.292260", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T18-57-41.292260.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T18-57-41.292260.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_13T18_57_41.292260", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T18-57-41.292260.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T18-57-41.292260.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_13T18_57_41.292260", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T18-57-41.292260.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T18-57-41.292260.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_13T18_57_41.292260", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T18-57-41.292260.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T18-57-41.292260.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T18_57_41.292260", "path": ["**/details_harness|winogrande|5_2024-01-13T18-57-41.292260.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T18-57-41.292260.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T18_57_41.292260", "path": ["results_2024-01-13T18-57-41.292260.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T18-57-41.292260.parquet"]}]}]} | 2024-01-13T19:00:22+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of udkai/Turdus
Dataset automatically created during the evaluation run of model udkai/Turdus on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-13T18:57:41.292260(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of udkai/Turdus\n\n\n\nDataset automatically created during the evaluation run of model udkai/Turdus on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T18:57:41.292260(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of udkai/Turdus\n\n\n\nDataset automatically created during the evaluation run of model udkai/Turdus on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T18:57:41.292260(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
55da3294b5612884cc24ba75c5ad1ce5d42b6455 |
# Dataset Card for Evaluation run of Unbabel/TowerInstruct-7B-v0.1
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Unbabel/TowerInstruct-7B-v0.1](https://huggingface.co/Unbabel/TowerInstruct-7B-v0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Unbabel__TowerInstruct-7B-v0.1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T18:58:50.073000](https://huggingface.co/datasets/open-llm-leaderboard/details_Unbabel__TowerInstruct-7B-v0.1/blob/main/results_2024-01-13T18-58-50.073000.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.4711217152311766,
"acc_stderr": 0.03442367854889606,
"acc_norm": 0.4757369265971281,
"acc_norm_stderr": 0.03519302105112233,
"mc1": 0.29253365973072215,
"mc1_stderr": 0.015925597445286165,
"mc2": 0.42594704830683766,
"mc2_stderr": 0.014921954316600566
},
"harness|arc:challenge|25": {
"acc": 0.5110921501706485,
"acc_stderr": 0.014607794914013048,
"acc_norm": 0.5546075085324232,
"acc_norm_stderr": 0.014523987638344076
},
"harness|hellaswag|10": {
"acc": 0.5993825931089425,
"acc_stderr": 0.004890221012015062,
"acc_norm": 0.789982075283808,
"acc_norm_stderr": 0.004064885496003441
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.45185185185185184,
"acc_stderr": 0.04299268905480864,
"acc_norm": 0.45185185185185184,
"acc_norm_stderr": 0.04299268905480864
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.4276315789473684,
"acc_stderr": 0.04026097083296558,
"acc_norm": 0.4276315789473684,
"acc_norm_stderr": 0.04026097083296558
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.51,
"acc_stderr": 0.05024183937956911,
"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956911
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.4716981132075472,
"acc_stderr": 0.0307235352490061,
"acc_norm": 0.4716981132075472,
"acc_norm_stderr": 0.0307235352490061
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.4236111111111111,
"acc_stderr": 0.04132125019723369,
"acc_norm": 0.4236111111111111,
"acc_norm_stderr": 0.04132125019723369
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.4,
"acc_stderr": 0.049236596391733084,
"acc_norm": 0.4,
"acc_norm_stderr": 0.049236596391733084
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.34,
"acc_stderr": 0.047609522856952344,
"acc_norm": 0.34,
"acc_norm_stderr": 0.047609522856952344
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.3988439306358382,
"acc_stderr": 0.037336266553835096,
"acc_norm": 0.3988439306358382,
"acc_norm_stderr": 0.037336266553835096
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.22549019607843138,
"acc_stderr": 0.041583075330832865,
"acc_norm": 0.22549019607843138,
"acc_norm_stderr": 0.041583075330832865
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.57,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.57,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.44680851063829785,
"acc_stderr": 0.0325005368436584,
"acc_norm": 0.44680851063829785,
"acc_norm_stderr": 0.0325005368436584
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.32456140350877194,
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"acc_norm": 0.32456140350877194,
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}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Who are the source data producers?
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#### Annotation process
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#### Personal and Sensitive Information
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## Bias, Risks, and Limitations
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_Unbabel__TowerInstruct-7B-v0.1 | [
"region:us"
] | 2024-01-13T19:01:12+00:00 | {"pretty_name": "Evaluation run of Unbabel/TowerInstruct-7B-v0.1", "dataset_summary": "Dataset automatically created during the evaluation run of model [Unbabel/TowerInstruct-7B-v0.1](https://huggingface.co/Unbabel/TowerInstruct-7B-v0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Unbabel__TowerInstruct-7B-v0.1\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T18:58:50.073000](https://huggingface.co/datasets/open-llm-leaderboard/details_Unbabel__TowerInstruct-7B-v0.1/blob/main/results_2024-01-13T18-58-50.073000.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.4711217152311766,\n \"acc_stderr\": 0.03442367854889606,\n \"acc_norm\": 0.4757369265971281,\n \"acc_norm_stderr\": 0.03519302105112233,\n \"mc1\": 0.29253365973072215,\n \"mc1_stderr\": 0.015925597445286165,\n \"mc2\": 0.42594704830683766,\n \"mc2_stderr\": 0.014921954316600566\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5110921501706485,\n \"acc_stderr\": 0.014607794914013048,\n \"acc_norm\": 0.5546075085324232,\n \"acc_norm_stderr\": 0.014523987638344076\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5993825931089425,\n \"acc_stderr\": 0.004890221012015062,\n \"acc_norm\": 0.789982075283808,\n \"acc_norm_stderr\": 0.004064885496003441\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.45185185185185184,\n \"acc_stderr\": 0.04299268905480864,\n \"acc_norm\": 0.45185185185185184,\n \"acc_norm_stderr\": 0.04299268905480864\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.4276315789473684,\n \"acc_stderr\": 0.04026097083296558,\n \"acc_norm\": 0.4276315789473684,\n \"acc_norm_stderr\": 0.04026097083296558\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.4716981132075472,\n \"acc_stderr\": 0.0307235352490061,\n \"acc_norm\": 0.4716981132075472,\n \"acc_norm_stderr\": 0.0307235352490061\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4236111111111111,\n \"acc_stderr\": 0.04132125019723369,\n \"acc_norm\": 0.4236111111111111,\n \"acc_norm_stderr\": 0.04132125019723369\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.047609522856952344,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.047609522856952344\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.3988439306358382,\n \"acc_stderr\": 0.037336266553835096,\n \"acc_norm\": 0.3988439306358382,\n \"acc_norm_stderr\": 0.037336266553835096\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.041583075330832865,\n \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.041583075330832865\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.57,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.44680851063829785,\n \"acc_stderr\": 0.0325005368436584,\n \"acc_norm\": 0.44680851063829785,\n \"acc_norm_stderr\": 0.0325005368436584\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.32456140350877194,\n \"acc_stderr\": 0.04404556157374767,\n \"acc_norm\": 0.32456140350877194,\n \"acc_norm_stderr\": 0.04404556157374767\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.4413793103448276,\n \"acc_stderr\": 0.04137931034482758,\n \"acc_norm\": 0.4413793103448276,\n \"acc_norm_stderr\": 0.04137931034482758\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.25925925925925924,\n \"acc_stderr\": 0.022569897074918407,\n \"acc_norm\": 0.25925925925925924,\n \"acc_norm_stderr\": 0.022569897074918407\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2857142857142857,\n \"acc_stderr\": 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["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T18-58-50.073000.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2024_01_13T18_58_50.073000", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T18-58-50.073000.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T18-58-50.073000.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2024_01_13T18_58_50.073000", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T18-58-50.073000.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T18-58-50.073000.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2024_01_13T18_58_50.073000", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T18-58-50.073000.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T18-58-50.073000.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_01_13T18_58_50.073000", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T18-58-50.073000.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T18-58-50.073000.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_01_13T18_58_50.073000", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T18-58-50.073000.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T18-58-50.073000.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_01_13T18_58_50.073000", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T18-58-50.073000.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T18-58-50.073000.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_01_13T18_58_50.073000", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T18-58-50.073000.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T18-58-50.073000.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_01_13T18_58_50.073000", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T18-58-50.073000.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T18-58-50.073000.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_01_13T18_58_50.073000", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T18-58-50.073000.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T18-58-50.073000.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_13T18_58_50.073000", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T18-58-50.073000.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T18-58-50.073000.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_13T18_58_50.073000", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T18-58-50.073000.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T18-58-50.073000.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_13T18_58_50.073000", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T18-58-50.073000.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T18-58-50.073000.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T18_58_50.073000", "path": ["**/details_harness|winogrande|5_2024-01-13T18-58-50.073000.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T18-58-50.073000.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T18_58_50.073000", "path": ["results_2024-01-13T18-58-50.073000.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T18-58-50.073000.parquet"]}]}]} | 2024-01-13T19:01:33+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of Unbabel/TowerInstruct-7B-v0.1
Dataset automatically created during the evaluation run of model Unbabel/TowerInstruct-7B-v0.1 on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-13T18:58:50.073000(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of Unbabel/TowerInstruct-7B-v0.1\n\n\n\nDataset automatically created during the evaluation run of model Unbabel/TowerInstruct-7B-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T18:58:50.073000(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of Unbabel/TowerInstruct-7B-v0.1\n\n\n\nDataset automatically created during the evaluation run of model Unbabel/TowerInstruct-7B-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T18:58:50.073000(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
234d5e3bb2c0f12d9190596e3d34efbc69b81800 |
# Dataset of georgia/ジョージア/佐治亚 (Azur Lane)
This is the dataset of georgia/ジョージア/佐治亚 (Azur Lane), containing 35 images and their tags.
The core tags of this character are `breasts, blue_eyes, earrings, black_hair, large_breasts, bangs, heterochromia, hair_ornament, yellow_eyes, long_hair, hair_between_eyes, star_earrings`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 35 | 48.40 MiB | [Download](https://huggingface.co/datasets/CyberHarem/georgia_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 35 | 26.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/georgia_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 76 | 49.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/georgia_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 35 | 42.32 MiB | [Download](https://huggingface.co/datasets/CyberHarem/georgia_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 76 | 73.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/georgia_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/georgia_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 5 |  |  |  |  |  | 1girl, cleavage, jewelry, simple_background, solo, looking_at_viewer, smile, white_background, brown_hair, choker, gloves, star_(symbol), upper_body |
| 1 | 6 |  |  |  |  |  | 1girl, ahoge, arm_strap, black_bikini, eyewear_on_head, jewelry, looking_at_viewer, solo, star_(symbol), sunglasses, short_hair_with_long_locks, bare_shoulders, navel, side-tie_bikini_bottom, simple_background, smile, choker, leg_ribbon, open_mouth, tankini, thigh_strap, white_background |
| 2 | 9 |  |  |  |  |  | 1girl, solo, cleavage, gloves, jewelry, thigh_strap, rigging, skirt, looking_at_viewer, short_hair, strapless, thighs, turret, asymmetrical_legwear, boots, cannon, full_body, single_thighhigh |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | cleavage | jewelry | simple_background | solo | looking_at_viewer | smile | white_background | brown_hair | choker | gloves | star_(symbol) | upper_body | ahoge | arm_strap | black_bikini | eyewear_on_head | sunglasses | short_hair_with_long_locks | bare_shoulders | navel | side-tie_bikini_bottom | leg_ribbon | open_mouth | tankini | thigh_strap | rigging | skirt | short_hair | strapless | thighs | turret | asymmetrical_legwear | boots | cannon | full_body | single_thighhigh |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------|:----------|:--------------------|:-------|:--------------------|:--------|:-------------------|:-------------|:---------|:---------|:----------------|:-------------|:--------|:------------|:---------------|:------------------|:-------------|:-----------------------------|:-----------------|:--------|:-------------------------|:-------------|:-------------|:----------|:--------------|:----------|:--------|:-------------|:------------|:---------|:---------|:-----------------------|:--------|:---------|:------------|:-------------------|
| 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | | X | X | X | X | X | X | | X | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | |
| 2 | 9 |  |  |  |  |  | X | X | X | | X | X | | | | | X | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/georgia_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T19:21:12+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T19:32:12+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of georgia/ジョージア/佐治亚 (Azur Lane)
========================================
This is the dataset of georgia/ジョージア/佐治亚 (Azur Lane), containing 35 images and their tags.
The core tags of this character are 'breasts, blue\_eyes, earrings, black\_hair, large\_breasts, bangs, heterochromia, hair\_ornament, yellow\_eyes, long\_hair, hair\_between\_eyes, star\_earrings', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
084170f0f049a0b56181ab510950d58897f7be5b |
# Dataset of jade/ヤーデ/亚德 (Azur Lane)
This is the dataset of jade/ヤーデ/亚德 (Azur Lane), containing 46 images and their tags.
The core tags of this character are `breasts, blue_eyes, bangs, grey_hair, hair_bun, hair_ornament, large_breasts, short_hair, hair_between_eyes, hairclip, hat, double_bun, mole`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 46 | 92.03 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jade_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 46 | 43.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jade_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 117 | 93.81 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jade_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 46 | 78.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jade_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 117 | 153.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jade_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/jade_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 16 |  |  |  |  |  | 1girl, looking_at_viewer, solo, popsicle, sailor_collar, bracelet, white_one-piece_swimsuit, blush, bare_shoulders, innertube, water, covered_navel, holding, looking_back, smile |
| 1 | 25 |  |  |  |  |  | 1girl, looking_at_viewer, solo, cleavage, smile, blush, long_sleeves, white_background, simple_background, white_gloves, black_headwear, thigh_strap, black_dress, skirt, cross, mole_under_eye |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | popsicle | sailor_collar | bracelet | white_one-piece_swimsuit | blush | bare_shoulders | innertube | water | covered_navel | holding | looking_back | smile | cleavage | long_sleeves | white_background | simple_background | white_gloves | black_headwear | thigh_strap | black_dress | skirt | cross | mole_under_eye |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:-----------|:----------------|:-----------|:---------------------------|:--------|:-----------------|:------------|:--------|:----------------|:----------|:---------------|:--------|:-----------|:---------------|:-------------------|:--------------------|:---------------|:-----------------|:--------------|:--------------|:--------|:--------|:-----------------|
| 0 | 16 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | |
| 1 | 25 |  |  |  |  |  | X | X | X | | | | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/jade_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T19:21:13+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T19:33:01+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of jade/ヤーデ/亚德 (Azur Lane)
==================================
This is the dataset of jade/ヤーデ/亚德 (Azur Lane), containing 46 images and their tags.
The core tags of this character are 'breasts, blue\_eyes, bangs, grey\_hair, hair\_bun, hair\_ornament, large\_breasts, short\_hair, hair\_between\_eyes, hairclip, hat, double\_bun, mole', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
4c3ef8819ca8e9b45fdd2bc427753678e3302820 |
# Dataset of odin/オーディン/奥丁 (Azur Lane)
This is the dataset of odin/オーディン/奥丁 (Azur Lane), containing 42 images and their tags.
The core tags of this character are `multicolored_hair, white_hair, red_hair, blue_eyes, long_hair, streaked_hair, hair_over_one_eye, hat, peaked_cap, military_hat, black_headwear, breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 42 | 70.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/odin_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 42 | 35.91 MiB | [Download](https://huggingface.co/datasets/CyberHarem/odin_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 92 | 74.33 MiB | [Download](https://huggingface.co/datasets/CyberHarem/odin_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 42 | 61.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/odin_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 92 | 111.20 MiB | [Download](https://huggingface.co/datasets/CyberHarem/odin_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/odin_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------|
| 0 | 42 |  |  |  |  |  | 1girl, solo, looking_at_viewer, black_coat, iron_cross, breastplate, sword, open_coat, holding, sheath |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | black_coat | iron_cross | breastplate | sword | open_coat | holding | sheath |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:-------------|:-------------|:--------------|:--------|:------------|:----------|:---------|
| 0 | 42 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/odin_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T19:21:13+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T19:30:27+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of odin/オーディン/奥丁 (Azur Lane)
====================================
This is the dataset of odin/オーディン/奥丁 (Azur Lane), containing 42 images and their tags.
The core tags of this character are 'multicolored\_hair, white\_hair, red\_hair, blue\_eyes, long\_hair, streaked\_hair, hair\_over\_one\_eye, hat, peaked\_cap, military\_hat, black\_headwear, breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
611a973fc613cd15ba77ca0bed72afa8a3e6fc3a |
# Dataset of aoba/青葉/青叶 (Azur Lane)
This is the dataset of aoba/青葉/青叶 (Azur Lane), containing 10 images and their tags.
The core tags of this character are `long_hair, bangs, aqua_hair, breasts, brown_eyes, animal_ears, twintails, medium_breasts, blue_hair, earrings, hair_between_eyes, large_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:---------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 10 | 8.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aoba_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 10 | 5.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aoba_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 19 | 9.94 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aoba_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 10 | 7.47 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aoba_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 19 | 13.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aoba_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/aoba_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 10 |  |  |  |  |  | 1girl, smile, solo, cleavage, looking_at_viewer, open_jacket, pleated_skirt, black_shirt, simple_background, white_skirt, black_jacket, blush, collarbone, white_background, holding_pen, jewelry, miniskirt, open_mouth |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | smile | solo | cleavage | looking_at_viewer | open_jacket | pleated_skirt | black_shirt | simple_background | white_skirt | black_jacket | blush | collarbone | white_background | holding_pen | jewelry | miniskirt | open_mouth |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-------|:-----------|:--------------------|:--------------|:----------------|:--------------|:--------------------|:--------------|:---------------|:--------|:-------------|:-------------------|:--------------|:----------|:------------|:-------------|
| 0 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/aoba_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T19:21:16+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T19:24:55+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of aoba/青葉/青叶 (Azur Lane)
=================================
This is the dataset of aoba/青葉/青叶 (Azur Lane), containing 10 images and their tags.
The core tags of this character are 'long\_hair, bangs, aqua\_hair, breasts, brown\_eyes, animal\_ears, twintails, medium\_breasts, blue\_hair, earrings, hair\_between\_eyes, large\_breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
b5b97f1c58eefb1baa7335fe117a2a3c7d17282c |
# Dataset of comet/コメット/彗星 (Azur Lane)
This is the dataset of comet/コメット/彗星 (Azur Lane), containing 34 images and their tags.
The core tags of this character are `green_hair, long_hair, red_eyes, twintails, ahoge, hat, bangs, beret, breasts, hair_between_eyes, hair_ornament, white_headwear, ribbon, very_long_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 34 | 41.48 MiB | [Download](https://huggingface.co/datasets/CyberHarem/comet_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 34 | 25.11 MiB | [Download](https://huggingface.co/datasets/CyberHarem/comet_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 77 | 50.55 MiB | [Download](https://huggingface.co/datasets/CyberHarem/comet_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 34 | 36.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/comet_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 77 | 67.95 MiB | [Download](https://huggingface.co/datasets/CyberHarem/comet_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/comet_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 17 |  |  |  |  |  | smile, 1girl, solo, open_mouth, star_(symbol), blush, choker, looking_at_viewer, puffy_sleeves, white_thighhighs, blue_skirt, plaid_skirt, white_shirt, long_sleeves, collared_shirt, one_eye_closed, ;d, hair_ribbon, pleated_skirt, retrofit_(azur_lane), white_background |
| 1 | 5 |  |  |  |  |  | 2girls, smile, 1girl, blush, looking_at_viewer, open_mouth, solo_focus, blonde_hair, thighhighs, collarbone, one_eye_closed, skirt, standing |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | smile | 1girl | solo | open_mouth | star_(symbol) | blush | choker | looking_at_viewer | puffy_sleeves | white_thighhighs | blue_skirt | plaid_skirt | white_shirt | long_sleeves | collared_shirt | one_eye_closed | ;d | hair_ribbon | pleated_skirt | retrofit_(azur_lane) | white_background | 2girls | solo_focus | blonde_hair | thighhighs | collarbone | skirt | standing |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-------|:-------------|:----------------|:--------|:---------|:--------------------|:----------------|:-------------------|:-------------|:--------------|:--------------|:---------------|:-----------------|:-----------------|:-----|:--------------|:----------------|:-----------------------|:-------------------|:---------|:-------------|:--------------|:-------------|:-------------|:--------|:-----------|
| 0 | 17 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | | X | | X | | X | | | | | | | | X | | | | | | X | X | X | X | X | X | X |
| CyberHarem/comet_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T19:21:22+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T19:31:04+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of comet/コメット/彗星 (Azur Lane)
====================================
This is the dataset of comet/コメット/彗星 (Azur Lane), containing 34 images and their tags.
The core tags of this character are 'green\_hair, long\_hair, red\_eyes, twintails, ahoge, hat, bangs, beret, breasts, hair\_between\_eyes, hair\_ornament, white\_headwear, ribbon, very\_long\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
8a11bf5b81e75a4ae201136e7a2d9112f46dc2b4 |
# Dataset of joffre/ジョッフル/霞飞 (Azur Lane)
This is the dataset of joffre/ジョッフル/霞飞 (Azur Lane), containing 73 images and their tags.
The core tags of this character are `breasts, twintails, large_breasts, hair_ornament, bangs, red_eyes, grey_hair, long_hair, white_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 73 | 135.42 MiB | [Download](https://huggingface.co/datasets/CyberHarem/joffre_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 73 | 65.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/joffre_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 190 | 151.33 MiB | [Download](https://huggingface.co/datasets/CyberHarem/joffre_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 73 | 114.40 MiB | [Download](https://huggingface.co/datasets/CyberHarem/joffre_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 190 | 230.14 MiB | [Download](https://huggingface.co/datasets/CyberHarem/joffre_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/joffre_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 12 |  |  |  |  |  | 1girl, cleavage, holding_sword, looking_at_viewer, solo, white_dress, black_gloves, black_choker, fingerless_gloves, white_thighhighs, wide_sleeves, feathered_wings, juliet_sleeves, simple_background, black_wings, medium_breasts, white_background |
| 1 | 7 |  |  |  |  |  | 1girl, looking_at_viewer, solo, white_dress, black_gloves, cleavage, white_thighhighs, fingerless_gloves, parted_lips, black_choker, juliet_sleeves, thighs, medium_breasts, wide_sleeves |
| 2 | 5 |  |  |  |  |  | 1girl, blue_dress, cleavage, crown, hair_bow, looking_at_viewer, sitting, solo, white_thighhighs, pink_eyes, bare_shoulders, black_bow, blue_footwear, butterfly, garter_straps, high_heels, red_cape, wrist_cuffs, ass, detached_sleeves, frills, hair_between_eyes, official_alternate_costume, parted_lips, puffy_sleeves, sidelocks, simple_background, smile, white_background |
| 3 | 6 |  |  |  |  |  | 1boy, 1girl, blush, hetero, nipples, open_mouth, black_gloves, penis, solo_focus, bar_censor, fingerless_gloves, sex, vaginal, black_choker, breasts_out, cross-section, cum_in_pussy, cum_on_breasts, grabbing, internal_cumshot, purple_eyes, sweat, uterus |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | cleavage | holding_sword | looking_at_viewer | solo | white_dress | black_gloves | black_choker | fingerless_gloves | white_thighhighs | wide_sleeves | feathered_wings | juliet_sleeves | simple_background | black_wings | medium_breasts | white_background | parted_lips | thighs | blue_dress | crown | hair_bow | sitting | pink_eyes | bare_shoulders | black_bow | blue_footwear | butterfly | garter_straps | high_heels | red_cape | wrist_cuffs | ass | detached_sleeves | frills | hair_between_eyes | official_alternate_costume | puffy_sleeves | sidelocks | smile | 1boy | blush | hetero | nipples | open_mouth | penis | solo_focus | bar_censor | sex | vaginal | breasts_out | cross-section | cum_in_pussy | cum_on_breasts | grabbing | internal_cumshot | purple_eyes | sweat | uterus |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------|:----------------|:--------------------|:-------|:--------------|:---------------|:---------------|:--------------------|:-------------------|:---------------|:------------------|:-----------------|:--------------------|:--------------|:-----------------|:-------------------|:--------------|:---------|:-------------|:--------|:-----------|:----------|:------------|:-----------------|:------------|:----------------|:------------|:----------------|:-------------|:-----------|:--------------|:------|:-------------------|:---------|:--------------------|:-----------------------------|:----------------|:------------|:--------|:-------|:--------|:---------|:----------|:-------------|:--------|:-------------|:-------------|:------|:----------|:--------------|:----------------|:---------------|:-----------------|:-----------|:-------------------|:--------------|:--------|:---------|
| 0 | 12 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 7 |  |  |  |  |  | X | X | | X | X | X | X | X | X | X | X | | X | | | X | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | X | X | | X | X | | | | | X | | | | X | | | X | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | |
| 3 | 6 |  |  |  |  |  | X | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/joffre_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T19:21:29+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T19:43:06+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of joffre/ジョッフル/霞飞 (Azur Lane)
======================================
This is the dataset of joffre/ジョッフル/霞飞 (Azur Lane), containing 73 images and their tags.
The core tags of this character are 'breasts, twintails, large\_breasts, hair\_ornament, bangs, red\_eyes, grey\_hair, long\_hair, white\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
b80d4a52237e6edd9b59045bc5b7aeeb4bebf175 |
# Dataset Card for Evaluation run of KnutJaegersberg/Qwen-14B-Llamafied
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [KnutJaegersberg/Qwen-14B-Llamafied](https://huggingface.co/KnutJaegersberg/Qwen-14B-Llamafied) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_KnutJaegersberg__Qwen-14B-Llamafied",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T19:31:00.889052](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__Qwen-14B-Llamafied/blob/main/results_2024-01-13T19-31-00.889052.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6576627342855309,
"acc_stderr": 0.032068617008274285,
"acc_norm": 0.661971967843754,
"acc_norm_stderr": 0.03270262072736983,
"mc1": 0.30966952264381886,
"mc1_stderr": 0.016185744355144912,
"mc2": 0.4560156985268844,
"mc2_stderr": 0.014814301713999594
},
"harness|arc:challenge|25": {
"acc": 0.507679180887372,
"acc_stderr": 0.01460966744089257,
"acc_norm": 0.5520477815699659,
"acc_norm_stderr": 0.014532011498211676
},
"harness|hellaswag|10": {
"acc": 0.6353316072495518,
"acc_stderr": 0.004803533333364225,
"acc_norm": 0.8231428002389962,
"acc_norm_stderr": 0.003807680331172903
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.38,
"acc_stderr": 0.048783173121456316,
"acc_norm": 0.38,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5925925925925926,
"acc_stderr": 0.042446332383532286,
"acc_norm": 0.5925925925925926,
"acc_norm_stderr": 0.042446332383532286
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6907894736842105,
"acc_stderr": 0.037610708698674805,
"acc_norm": 0.6907894736842105,
"acc_norm_stderr": 0.037610708698674805
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.73,
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"acc_norm": 0.73,
"acc_norm_stderr": 0.0446196043338474
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.690566037735849,
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"acc_norm": 0.690566037735849,
"acc_norm_stderr": 0.028450154794118637
},
"harness|hendrycksTest-college_biology|5": {
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"acc_norm": 0.7847222222222222,
"acc_norm_stderr": 0.03437079344106134
},
"harness|hendrycksTest-college_chemistry|5": {
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"acc_norm": 0.52,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.58,
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"acc_norm": 0.58,
"acc_norm_stderr": 0.04960449637488584
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.44,
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"acc_norm": 0.44,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6763005780346821,
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"acc_norm": 0.6763005780346821,
"acc_norm_stderr": 0.0356760379963917
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.46078431372549017,
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"acc_norm": 0.46078431372549017,
"acc_norm_stderr": 0.049598599663841815
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.75,
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"acc_norm": 0.75,
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},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.6382978723404256,
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},
"harness|hendrycksTest-econometrics|5": {
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"acc_norm": 0.4824561403508772,
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},
"harness|hendrycksTest-electrical_engineering|5": {
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},
"harness|hendrycksTest-elementary_mathematics|5": {
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},
"harness|hendrycksTest-formal_logic|5": {
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},
"harness|hendrycksTest-global_facts|5": {
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"acc_norm": 0.44,
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},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.8193548387096774,
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"acc_norm": 0.8193548387096774,
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},
"harness|hendrycksTest-high_school_chemistry|5": {
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"acc_norm": 0.6354679802955665,
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},
"harness|hendrycksTest-high_school_computer_science|5": {
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},
"harness|hendrycksTest-high_school_european_history|5": {
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},
"harness|hendrycksTest-high_school_geography|5": {
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},
"harness|hendrycksTest-high_school_government_and_politics|5": {
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},
"harness|hendrycksTest-high_school_macroeconomics|5": {
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"acc_norm": 0.6897435897435897,
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},
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},
"harness|hendrycksTest-high_school_microeconomics|5": {
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"acc_norm": 0.7184873949579832,
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},
"harness|hendrycksTest-high_school_physics|5": {
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"acc_norm": 0.4105960264900662,
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},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8311926605504587,
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"acc_norm": 0.8311926605504587,
"acc_norm_stderr": 0.016060056268530368
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5787037037037037,
"acc_stderr": 0.03367462138896078,
"acc_norm": 0.5787037037037037,
"acc_norm_stderr": 0.03367462138896078
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.6225490196078431,
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"acc_norm": 0.6225490196078431,
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},
"harness|hendrycksTest-high_school_world_history|5": {
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"acc_norm": 0.8396624472573839,
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},
"harness|hendrycksTest-human_aging|5": {
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"acc_norm": 0.7354260089686099,
"acc_norm_stderr": 0.029605103217038332
},
"harness|hendrycksTest-human_sexuality|5": {
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"acc_norm": 0.7709923664122137,
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},
"harness|hendrycksTest-international_law|5": {
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},
"harness|hendrycksTest-jurisprudence|5": {
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"acc_norm": 0.7685185185185185,
"acc_norm_stderr": 0.04077494709252626
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7607361963190185,
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"acc_norm": 0.7607361963190185,
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},
"harness|hendrycksTest-machine_learning|5": {
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"acc_norm": 0.5892857142857143,
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},
"harness|hendrycksTest-management|5": {
"acc": 0.7766990291262136,
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"acc_norm": 0.7766990291262136,
"acc_norm_stderr": 0.04123553189891431
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8888888888888888,
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"acc_norm": 0.8888888888888888,
"acc_norm_stderr": 0.020588491316092375
},
"harness|hendrycksTest-medical_genetics|5": {
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"acc_norm": 0.73,
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},
"harness|hendrycksTest-miscellaneous|5": {
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},
"harness|hendrycksTest-moral_disputes|5": {
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},
"harness|hendrycksTest-moral_scenarios|5": {
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"acc_norm": 0.4770949720670391,
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},
"harness|hendrycksTest-nutrition|5": {
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"acc_norm": 0.7450980392156863,
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},
"harness|hendrycksTest-philosophy|5": {
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"acc_norm": 0.7363344051446945,
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},
"harness|hendrycksTest-prehistory|5": {
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},
"harness|hendrycksTest-professional_accounting|5": {
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},
"harness|hendrycksTest-professional_law|5": {
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},
"harness|hendrycksTest-professional_medicine|5": {
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"acc_norm_stderr": 0.02806499816704009
},
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},
"harness|hendrycksTest-public_relations|5": {
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},
"harness|hendrycksTest-security_studies|5": {
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"acc_stderr": 0.027833023871399683,
"acc_norm": 0.746938775510204,
"acc_norm_stderr": 0.027833023871399683
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8407960199004975,
"acc_stderr": 0.025870646766169136,
"acc_norm": 0.8407960199004975,
"acc_norm_stderr": 0.025870646766169136
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.83,
"acc_stderr": 0.03775251680686371,
"acc_norm": 0.83,
"acc_norm_stderr": 0.03775251680686371
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5240963855421686,
"acc_stderr": 0.03887971849597264,
"acc_norm": 0.5240963855421686,
"acc_norm_stderr": 0.03887971849597264
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8070175438596491,
"acc_stderr": 0.030267457554898458,
"acc_norm": 0.8070175438596491,
"acc_norm_stderr": 0.030267457554898458
},
"harness|truthfulqa:mc|0": {
"mc1": 0.30966952264381886,
"mc1_stderr": 0.016185744355144912,
"mc2": 0.4560156985268844,
"mc2_stderr": 0.014814301713999594
},
"harness|winogrande|5": {
"acc": 0.7655880031570639,
"acc_stderr": 0.011906130106237986
},
"harness|gsm8k|5": {
"acc": 0.5276724791508719,
"acc_stderr": 0.013751375538801326
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
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## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
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#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_KnutJaegersberg__Qwen-14B-Llamafied | [
"region:us"
] | 2024-01-13T19:33:11+00:00 | {"pretty_name": "Evaluation run of KnutJaegersberg/Qwen-14B-Llamafied", "dataset_summary": "Dataset automatically created during the evaluation run of model [KnutJaegersberg/Qwen-14B-Llamafied](https://huggingface.co/KnutJaegersberg/Qwen-14B-Llamafied) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_KnutJaegersberg__Qwen-14B-Llamafied\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T19:31:00.889052](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__Qwen-14B-Llamafied/blob/main/results_2024-01-13T19-31-00.889052.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6576627342855309,\n \"acc_stderr\": 0.032068617008274285,\n \"acc_norm\": 0.661971967843754,\n \"acc_norm_stderr\": 0.03270262072736983,\n \"mc1\": 0.30966952264381886,\n \"mc1_stderr\": 0.016185744355144912,\n \"mc2\": 0.4560156985268844,\n \"mc2_stderr\": 0.014814301713999594\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.507679180887372,\n \"acc_stderr\": 0.01460966744089257,\n \"acc_norm\": 0.5520477815699659,\n \"acc_norm_stderr\": 0.014532011498211676\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6353316072495518,\n \"acc_stderr\": 0.004803533333364225,\n \"acc_norm\": 0.8231428002389962,\n \"acc_norm_stderr\": 0.003807680331172903\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5925925925925926,\n \"acc_stderr\": 0.042446332383532286,\n \"acc_norm\": 0.5925925925925926,\n \"acc_norm_stderr\": 0.042446332383532286\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6907894736842105,\n \"acc_stderr\": 0.037610708698674805,\n \"acc_norm\": 0.6907894736842105,\n \"acc_norm_stderr\": 0.037610708698674805\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.73,\n \"acc_stderr\": 0.0446196043338474,\n \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.690566037735849,\n \"acc_stderr\": 0.028450154794118637,\n \"acc_norm\": 0.690566037735849,\n \"acc_norm_stderr\": 0.028450154794118637\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7847222222222222,\n \"acc_stderr\": 0.03437079344106134,\n \"acc_norm\": 0.7847222222222222,\n \"acc_norm_stderr\": 0.03437079344106134\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.58,\n \"acc_stderr\": 0.04960449637488584,\n \"acc_norm\": 0.58,\n \"acc_norm_stderr\": 0.04960449637488584\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6763005780346821,\n \"acc_stderr\": 0.0356760379963917,\n \"acc_norm\": 0.6763005780346821,\n \"acc_norm_stderr\": 0.0356760379963917\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.46078431372549017,\n \"acc_stderr\": 0.049598599663841815,\n \"acc_norm\": 0.46078431372549017,\n \"acc_norm_stderr\": 0.049598599663841815\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.6382978723404256,\n \"acc_stderr\": 0.03141082197596241,\n \"acc_norm\": 0.6382978723404256,\n \"acc_norm_stderr\": 0.03141082197596241\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4824561403508772,\n \"acc_stderr\": 0.0470070803355104,\n \"acc_norm\": 0.4824561403508772,\n \"acc_norm_stderr\": 0.0470070803355104\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.6758620689655173,\n \"acc_stderr\": 0.03900432069185554,\n \"acc_norm\": 0.6758620689655173,\n \"acc_norm_stderr\": 0.03900432069185554\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.5502645502645502,\n \"acc_stderr\": 0.02562085704293665,\n \"acc_norm\": 0.5502645502645502,\n \"acc_norm_stderr\": 0.02562085704293665\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.48412698412698413,\n \"acc_stderr\": 0.04469881854072606,\n \"acc_norm\": 0.48412698412698413,\n \"acc_norm_stderr\": 0.04469881854072606\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8193548387096774,\n \"acc_stderr\": 0.02188617856717253,\n \"acc_norm\": 0.8193548387096774,\n \"acc_norm_stderr\": 0.02188617856717253\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.6354679802955665,\n \"acc_stderr\": 0.033864057460620905,\n \"acc_norm\": 0.6354679802955665,\n \"acc_norm_stderr\": 0.033864057460620905\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.76,\n \"acc_stderr\": 0.04292346959909281,\n \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.04292346959909281\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03225078108306289,\n \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03225078108306289\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.8333333333333334,\n \"acc_stderr\": 0.026552207828215286,\n \"acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.026552207828215286\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.927461139896373,\n \"acc_stderr\": 0.018718998520678192,\n \"acc_norm\": 0.927461139896373,\n \"acc_norm_stderr\": 0.018718998520678192\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6897435897435897,\n \"acc_stderr\": 0.02345467488940429,\n \"acc_norm\": 0.6897435897435897,\n \"acc_norm_stderr\": 0.02345467488940429\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.37777777777777777,\n \"acc_stderr\": 0.02956070739246571,\n \"acc_norm\": 0.37777777777777777,\n \"acc_norm_stderr\": 0.02956070739246571\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.7184873949579832,\n \"acc_stderr\": 0.029213549414372174,\n \"acc_norm\": 0.7184873949579832,\n \"acc_norm_stderr\": 0.029213549414372174\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.4105960264900662,\n \"acc_stderr\": 0.04016689594849927,\n \"acc_norm\": 0.4105960264900662,\n \"acc_norm_stderr\": 0.04016689594849927\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8311926605504587,\n \"acc_stderr\": 0.016060056268530368,\n \"acc_norm\": 0.8311926605504587,\n \"acc_norm_stderr\": 0.016060056268530368\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5787037037037037,\n \"acc_stderr\": 0.03367462138896078,\n \"acc_norm\": 0.5787037037037037,\n \"acc_norm_stderr\": 0.03367462138896078\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.6225490196078431,\n \"acc_stderr\": 0.03402272044340703,\n \"acc_norm\": 0.6225490196078431,\n \"acc_norm_stderr\": 0.03402272044340703\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.8396624472573839,\n \"acc_stderr\": 0.02388438092596567,\n \"acc_norm\": 0.8396624472573839,\n \"acc_norm_stderr\": 0.02388438092596567\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7354260089686099,\n \"acc_stderr\": 0.029605103217038332,\n \"acc_norm\": 0.7354260089686099,\n \"acc_norm_stderr\": 0.029605103217038332\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.8264462809917356,\n \"acc_stderr\": 0.03457272836917671,\n \"acc_norm\": 0.8264462809917356,\n \"acc_norm_stderr\": 0.03457272836917671\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\n \"acc_stderr\": 0.04077494709252626,\n \"acc_norm\": 0.7685185185185185,\n \"acc_norm_stderr\": 0.04077494709252626\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.033519538795212696,\n \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.033519538795212696\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5892857142857143,\n \"acc_stderr\": 0.04669510663875192,\n \"acc_norm\": 0.5892857142857143,\n \"acc_norm_stderr\": 0.04669510663875192\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8888888888888888,\n \"acc_stderr\": 0.020588491316092375,\n \"acc_norm\": 0.8888888888888888,\n \"acc_norm_stderr\": 0.020588491316092375\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8301404853128991,\n \"acc_stderr\": 0.013428186370608303,\n \"acc_norm\": 0.8301404853128991,\n \"acc_norm_stderr\": 0.013428186370608303\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7369942196531792,\n \"acc_stderr\": 0.023703099525258172,\n \"acc_norm\": 0.7369942196531792,\n \"acc_norm_stderr\": 0.023703099525258172\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4770949720670391,\n \"acc_stderr\": 0.016704945740326188,\n \"acc_norm\": 0.4770949720670391,\n \"acc_norm_stderr\": 0.016704945740326188\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.7450980392156863,\n \"acc_stderr\": 0.024954184324879915,\n \"acc_norm\": 0.7450980392156863,\n \"acc_norm_stderr\": 0.024954184324879915\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7363344051446945,\n \"acc_stderr\": 0.02502553850053234,\n \"acc_norm\": 0.7363344051446945,\n \"acc_norm_stderr\": 0.02502553850053234\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.024922001168886335,\n \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.024922001168886335\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.4787234042553192,\n \"acc_stderr\": 0.029800481645628693,\n \"acc_norm\": 0.4787234042553192,\n \"acc_norm_stderr\": 0.029800481645628693\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4810951760104302,\n \"acc_stderr\": 0.012761104871472662,\n \"acc_norm\": 0.4810951760104302,\n \"acc_norm_stderr\": 0.012761104871472662\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.6911764705882353,\n \"acc_stderr\": 0.02806499816704009,\n \"acc_norm\": 0.6911764705882353,\n \"acc_norm_stderr\": 0.02806499816704009\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.7026143790849673,\n \"acc_stderr\": 0.018492596536396955,\n \"acc_norm\": 0.7026143790849673,\n \"acc_norm_stderr\": 0.018492596536396955\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6545454545454545,\n \"acc_stderr\": 0.04554619617541054,\n \"acc_norm\": 0.6545454545454545,\n \"acc_norm_stderr\": 0.04554619617541054\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.746938775510204,\n \"acc_stderr\": 0.027833023871399683,\n \"acc_norm\": 0.746938775510204,\n \"acc_norm_stderr\": 0.027833023871399683\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8407960199004975,\n \"acc_stderr\": 0.025870646766169136,\n \"acc_norm\": 0.8407960199004975,\n \"acc_norm_stderr\": 0.025870646766169136\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.83,\n \"acc_stderr\": 0.03775251680686371,\n \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.03775251680686371\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 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{"split": "latest", "path": ["**/details_harness|hendrycksTest-global_facts|5_2024-01-13T19-31-00.889052.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_biology_5", "data_files": [{"split": "2024_01_13T19_31_00.889052", "path": ["**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T19-31-00.889052.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T19-31-00.889052.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_chemistry_5", "data_files": [{"split": "2024_01_13T19_31_00.889052", "path": ["**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T19-31-00.889052.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T19-31-00.889052.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_computer_science_5", "data_files": [{"split": "2024_01_13T19_31_00.889052", "path": 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"path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-13T19-31-00.889052.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2024_01_13T19_31_00.889052", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T19-31-00.889052.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T19-31-00.889052.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2024_01_13T19_31_00.889052", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T19-31-00.889052.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T19-31-00.889052.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2024_01_13T19_31_00.889052", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T19-31-00.889052.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T19-31-00.889052.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2024_01_13T19_31_00.889052", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T19-31-00.889052.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T19-31-00.889052.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2024_01_13T19_31_00.889052", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T19-31-00.889052.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T19-31-00.889052.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2024_01_13T19_31_00.889052", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T19-31-00.889052.parquet"]}, {"split": "latest", "path": 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#region-us
|
# Dataset Card for Evaluation run of KnutJaegersberg/Qwen-14B-Llamafied
Dataset automatically created during the evaluation run of model KnutJaegersberg/Qwen-14B-Llamafied on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-13T19:31:00.889052(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of KnutJaegersberg/Qwen-14B-Llamafied\n\n\n\nDataset automatically created during the evaluation run of model KnutJaegersberg/Qwen-14B-Llamafied on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T19:31:00.889052(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of KnutJaegersberg/Qwen-14B-Llamafied\n\n\n\nDataset automatically created during the evaluation run of model KnutJaegersberg/Qwen-14B-Llamafied on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T19:31:00.889052(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
dd21cf0adfabfb17f1d2278ffbde8fe1a537c3d7 |
# Dataset Card for Evaluation run of KnutJaegersberg/internlm-20b-llamafied
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [KnutJaegersberg/internlm-20b-llamafied](https://huggingface.co/KnutJaegersberg/internlm-20b-llamafied) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_KnutJaegersberg__internlm-20b-llamafied",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T19:39:44.590825](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__internlm-20b-llamafied/blob/main/results_2024-01-13T19-39-44.590825.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.2529697662773502,
"acc_stderr": 0.03077338700338214,
"acc_norm": 0.2544077864541873,
"acc_norm_stderr": 0.031593813415922045,
"mc1": 0.2215422276621787,
"mc1_stderr": 0.014537867601301145,
"mc2": 0.4805606031451568,
"mc2_stderr": 0.016999605402858272
},
"harness|arc:challenge|25": {
"acc": 0.21928327645051193,
"acc_stderr": 0.012091245787615723,
"acc_norm": 0.26791808873720135,
"acc_norm_stderr": 0.012942030195136423
},
"harness|hellaswag|10": {
"acc": 0.25542720573590916,
"acc_stderr": 0.004352098082984431,
"acc_norm": 0.26399123680541725,
"acc_norm_stderr": 0.004398937225038417
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.27,
"acc_stderr": 0.044619604333847394,
"acc_norm": 0.27,
"acc_norm_stderr": 0.044619604333847394
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.21481481481481482,
"acc_stderr": 0.03547854198560826,
"acc_norm": 0.21481481481481482,
"acc_norm_stderr": 0.03547854198560826
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.28289473684210525,
"acc_stderr": 0.03665349695640767,
"acc_norm": 0.28289473684210525,
"acc_norm_stderr": 0.03665349695640767
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.22,
"acc_stderr": 0.04163331998932269,
"acc_norm": 0.22,
"acc_norm_stderr": 0.04163331998932269
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.2792452830188679,
"acc_stderr": 0.02761116340239972,
"acc_norm": 0.2792452830188679,
"acc_norm_stderr": 0.02761116340239972
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.2222222222222222,
"acc_stderr": 0.034765901043041336,
"acc_norm": 0.2222222222222222,
"acc_norm_stderr": 0.034765901043041336
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.29,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.29,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.23,
"acc_stderr": 0.04229525846816506,
"acc_norm": 0.23,
"acc_norm_stderr": 0.04229525846816506
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.23,
"acc_stderr": 0.04229525846816507,
"acc_norm": 0.23,
"acc_norm_stderr": 0.04229525846816507
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.28901734104046245,
"acc_stderr": 0.03456425745087,
"acc_norm": 0.28901734104046245,
"acc_norm_stderr": 0.03456425745087
},
"harness|hendrycksTest-college_physics|5": {
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"acc_stderr": 0.04092563958237655,
"acc_norm": 0.21568627450980393,
"acc_norm_stderr": 0.04092563958237655
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.27,
"acc_stderr": 0.044619604333847394,
"acc_norm": 0.27,
"acc_norm_stderr": 0.044619604333847394
},
"harness|hendrycksTest-conceptual_physics|5": {
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"acc_norm": 0.17446808510638298,
"acc_norm_stderr": 0.024809442335503973
},
"harness|hendrycksTest-econometrics|5": {
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"acc_stderr": 0.0409698513984367,
"acc_norm": 0.2543859649122807,
"acc_norm_stderr": 0.0409698513984367
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.2620689655172414,
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"acc_norm_stderr": 0.036646663372252565
},
"harness|hendrycksTest-elementary_mathematics|5": {
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"harness|hendrycksTest-formal_logic|5": {
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"acc_norm_stderr": 0.04104947269903394
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"harness|hendrycksTest-global_facts|5": {
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"acc_norm": 0.38,
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"harness|hendrycksTest-high_school_biology|5": {
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"acc_norm_stderr": 0.024362599693031103
},
"harness|hendrycksTest-high_school_chemistry|5": {
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"harness|hendrycksTest-high_school_computer_science|5": {
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},
"harness|hendrycksTest-high_school_european_history|5": {
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},
"harness|hendrycksTest-high_school_geography|5": {
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},
"harness|hendrycksTest-high_school_government_and_politics|5": {
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},
"harness|hendrycksTest-high_school_macroeconomics|5": {
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},
"harness|hendrycksTest-high_school_mathematics|5": {
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"acc_norm_stderr": 0.026962424325073835
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.2689075630252101,
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"acc_norm": 0.2689075630252101,
"acc_norm_stderr": 0.028801392193631276
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.2847682119205298,
"acc_stderr": 0.036848815213890225,
"acc_norm": 0.2847682119205298,
"acc_norm_stderr": 0.036848815213890225
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.24403669724770644,
"acc_stderr": 0.01841528635141641,
"acc_norm": 0.24403669724770644,
"acc_norm_stderr": 0.01841528635141641
},
"harness|hendrycksTest-high_school_statistics|5": {
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"acc_stderr": 0.0316746870682898,
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},
"harness|hendrycksTest-high_school_us_history|5": {
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},
"harness|hendrycksTest-high_school_world_history|5": {
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"acc_stderr": 0.027652153144159267,
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"acc_norm_stderr": 0.027652153144159267
},
"harness|hendrycksTest-human_aging|5": {
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"harness|hendrycksTest-human_sexuality|5": {
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"harness|hendrycksTest-international_law|5": {
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},
"harness|hendrycksTest-jurisprudence|5": {
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"harness|hendrycksTest-logical_fallacies|5": {
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},
"harness|hendrycksTest-management|5": {
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},
"harness|hendrycksTest-marketing|5": {
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"harness|hendrycksTest-medical_genetics|5": {
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},
"harness|hendrycksTest-miscellaneous|5": {
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"harness|hendrycksTest-moral_disputes|5": {
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},
"harness|hendrycksTest-moral_scenarios|5": {
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"harness|hendrycksTest-nutrition|5": {
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"harness|hendrycksTest-prehistory|5": {
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"harness|hendrycksTest-professional_accounting|5": {
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"harness|hendrycksTest-public_relations|5": {
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},
"harness|hendrycksTest-security_studies|5": {
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"acc_norm": 0.2816326530612245,
"acc_norm_stderr": 0.028795185574291286
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.22885572139303484,
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"acc_norm": 0.22885572139303484,
"acc_norm_stderr": 0.029705284056772426
},
"harness|hendrycksTest-us_foreign_policy|5": {
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"acc_norm": 0.22,
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},
"harness|hendrycksTest-virology|5": {
"acc": 0.2289156626506024,
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"acc_norm": 0.2289156626506024,
"acc_norm_stderr": 0.03270745277352477
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.23976608187134502,
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"acc_norm": 0.23976608187134502,
"acc_norm_stderr": 0.03274485211946956
},
"harness|truthfulqa:mc|0": {
"mc1": 0.2215422276621787,
"mc1_stderr": 0.014537867601301145,
"mc2": 0.4805606031451568,
"mc2_stderr": 0.016999605402858272
},
"harness|winogrande|5": {
"acc": 0.47829518547750594,
"acc_stderr": 0.01403923921648463
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_KnutJaegersberg__internlm-20b-llamafied | [
"region:us"
] | 2024-01-13T19:42:00+00:00 | {"pretty_name": "Evaluation run of KnutJaegersberg/internlm-20b-llamafied", "dataset_summary": "Dataset automatically created during the evaluation run of model [KnutJaegersberg/internlm-20b-llamafied](https://huggingface.co/KnutJaegersberg/internlm-20b-llamafied) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_KnutJaegersberg__internlm-20b-llamafied\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T19:39:44.590825](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__internlm-20b-llamafied/blob/main/results_2024-01-13T19-39-44.590825.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.2529697662773502,\n \"acc_stderr\": 0.03077338700338214,\n \"acc_norm\": 0.2544077864541873,\n \"acc_norm_stderr\": 0.031593813415922045,\n \"mc1\": 0.2215422276621787,\n \"mc1_stderr\": 0.014537867601301145,\n \"mc2\": 0.4805606031451568,\n \"mc2_stderr\": 0.016999605402858272\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.21928327645051193,\n \"acc_stderr\": 0.012091245787615723,\n \"acc_norm\": 0.26791808873720135,\n \"acc_norm_stderr\": 0.012942030195136423\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.25542720573590916,\n \"acc_stderr\": 0.004352098082984431,\n \"acc_norm\": 0.26399123680541725,\n \"acc_norm_stderr\": 0.004398937225038417\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.21481481481481482,\n \"acc_stderr\": 0.03547854198560826,\n \"acc_norm\": 0.21481481481481482,\n \"acc_norm_stderr\": 0.03547854198560826\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.28289473684210525,\n \"acc_stderr\": 0.03665349695640767,\n \"acc_norm\": 0.28289473684210525,\n \"acc_norm_stderr\": 0.03665349695640767\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.22,\n \"acc_stderr\": 0.04163331998932269,\n \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.04163331998932269\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.2792452830188679,\n \"acc_stderr\": 0.02761116340239972,\n \"acc_norm\": 0.2792452830188679,\n \"acc_norm_stderr\": 0.02761116340239972\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2222222222222222,\n \"acc_stderr\": 0.034765901043041336,\n \"acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.034765901043041336\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816507,\n \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816507\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.28901734104046245,\n \"acc_stderr\": 0.03456425745087,\n \"acc_norm\": 0.28901734104046245,\n \"acc_norm_stderr\": 0.03456425745087\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237655,\n \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237655\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.17446808510638298,\n \"acc_stderr\": 0.024809442335503973,\n \"acc_norm\": 0.17446808510638298,\n \"acc_norm_stderr\": 0.024809442335503973\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2543859649122807,\n \"acc_stderr\": 0.0409698513984367,\n \"acc_norm\": 0.2543859649122807,\n \"acc_norm_stderr\": 0.0409698513984367\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.2620689655172414,\n \"acc_stderr\": 0.036646663372252565,\n \"acc_norm\": 0.2620689655172414,\n \"acc_norm_stderr\": 0.036646663372252565\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.2619047619047619,\n \"acc_stderr\": 0.022644212615525214,\n \"acc_norm\": 0.2619047619047619,\n \"acc_norm_stderr\": 0.022644212615525214\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.30158730158730157,\n \"acc_stderr\": 0.04104947269903394,\n \"acc_norm\": 0.30158730158730157,\n \"acc_norm_stderr\": 0.04104947269903394\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.24193548387096775,\n \"acc_stderr\": 0.024362599693031103,\n \"acc_norm\": 0.24193548387096775,\n \"acc_norm_stderr\": 0.024362599693031103\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.2857142857142857,\n \"acc_stderr\": 0.0317852971064275,\n \"acc_norm\": 0.2857142857142857,\n \"acc_norm_stderr\": 0.0317852971064275\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.3393939393939394,\n \"acc_stderr\": 0.03697442205031595,\n \"acc_norm\": 0.3393939393939394,\n \"acc_norm_stderr\": 0.03697442205031595\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.30808080808080807,\n \"acc_stderr\": 0.03289477330098616,\n \"acc_norm\": 0.30808080808080807,\n \"acc_norm_stderr\": 0.03289477330098616\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.23316062176165803,\n \"acc_stderr\": 0.03051611137147602,\n \"acc_norm\": 0.23316062176165803,\n \"acc_norm_stderr\": 0.03051611137147602\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.2948717948717949,\n \"acc_stderr\": 0.023119362758232273,\n \"acc_norm\": 0.2948717948717949,\n \"acc_norm_stderr\": 0.023119362758232273\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.26666666666666666,\n \"acc_stderr\": 0.026962424325073835,\n \"acc_norm\": 0.26666666666666666,\n \"acc_norm_stderr\": 0.026962424325073835\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.2689075630252101,\n \"acc_stderr\": 0.028801392193631276,\n \"acc_norm\": 0.2689075630252101,\n \"acc_norm_stderr\": 0.028801392193631276\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.2847682119205298,\n \"acc_stderr\": 0.036848815213890225,\n \"acc_norm\": 0.2847682119205298,\n \"acc_norm_stderr\": 0.036848815213890225\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.24403669724770644,\n \"acc_stderr\": 0.01841528635141641,\n \"acc_norm\": 0.24403669724770644,\n \"acc_norm_stderr\": 0.01841528635141641\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.3148148148148148,\n \"acc_stderr\": 0.0316746870682898,\n \"acc_norm\": 0.3148148148148148,\n \"acc_norm_stderr\": 0.0316746870682898\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.25980392156862747,\n \"acc_stderr\": 0.030778554678693244,\n \"acc_norm\": 0.25980392156862747,\n \"acc_norm_stderr\": 0.030778554678693244\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.23628691983122363,\n \"acc_stderr\": 0.027652153144159267,\n \"acc_norm\": 0.23628691983122363,\n \"acc_norm_stderr\": 0.027652153144159267\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.17040358744394618,\n \"acc_stderr\": 0.025234593447136165,\n \"acc_norm\": 0.17040358744394618,\n \"acc_norm_stderr\": 0.025234593447136165\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.2900763358778626,\n \"acc_stderr\": 0.03980066246467766,\n \"acc_norm\": 0.2900763358778626,\n \"acc_norm_stderr\": 0.03980066246467766\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.21487603305785125,\n \"acc_stderr\": 0.037494924487096966,\n \"acc_norm\": 0.21487603305785125,\n \"acc_norm_stderr\": 0.037494924487096966\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.24074074074074073,\n \"acc_stderr\": 0.041331194402438376,\n \"acc_norm\": 0.24074074074074073,\n \"acc_norm_stderr\": 0.041331194402438376\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.3312883435582822,\n \"acc_stderr\": 0.03697983910025588,\n \"acc_norm\": 0.3312883435582822,\n \"acc_norm_stderr\": 0.03697983910025588\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.19642857142857142,\n \"acc_stderr\": 0.03770970049347018,\n \"acc_norm\": 0.19642857142857142,\n \"acc_norm_stderr\": 0.03770970049347018\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.2621359223300971,\n \"acc_stderr\": 0.043546310772605956,\n \"acc_norm\": 0.2621359223300971,\n \"acc_norm_stderr\": 0.043546310772605956\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.1581196581196581,\n \"acc_stderr\": 0.023902325549560392,\n \"acc_norm\": 0.1581196581196581,\n \"acc_norm_stderr\": 0.023902325549560392\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.17,\n \"acc_stderr\": 0.03775251680686371,\n \"acc_norm\": 0.17,\n \"acc_norm_stderr\": 0.03775251680686371\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.26947637292464877,\n \"acc_stderr\": 0.01586624307321505,\n \"acc_norm\": 0.26947637292464877,\n \"acc_norm_stderr\": 0.01586624307321505\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.18497109826589594,\n \"acc_stderr\": 0.020903975842083027,\n \"acc_norm\": 0.18497109826589594,\n \"acc_norm_stderr\": 0.020903975842083027\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2245810055865922,\n \"acc_stderr\": 0.01395680366654464,\n \"acc_norm\": 0.2245810055865922,\n \"acc_norm_stderr\": 0.01395680366654464\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.2647058823529412,\n \"acc_stderr\": 0.025261691219729474,\n \"acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.025261691219729474\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2861736334405145,\n \"acc_stderr\": 0.02567025924218895,\n \"acc_norm\": 0.2861736334405145,\n \"acc_norm_stderr\": 0.02567025924218895\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.26851851851851855,\n \"acc_stderr\": 0.02465968518596729,\n \"acc_norm\": 0.26851851851851855,\n \"acc_norm_stderr\": 0.02465968518596729\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.2801418439716312,\n \"acc_stderr\": 0.02678917235114024,\n \"acc_norm\": 0.2801418439716312,\n \"acc_norm_stderr\": 0.02678917235114024\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2653194263363755,\n \"acc_stderr\": 0.011276198843958878,\n \"acc_norm\": 0.2653194263363755,\n \"acc_norm_stderr\": 0.011276198843958878\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.29044117647058826,\n \"acc_stderr\": 0.027576468622740522,\n \"acc_norm\": 0.29044117647058826,\n \"acc_norm_stderr\": 0.027576468622740522\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.23202614379084968,\n \"acc_stderr\": 0.017077373377857016,\n \"acc_norm\": 0.23202614379084968,\n \"acc_norm_stderr\": 0.017077373377857016\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.18181818181818182,\n \"acc_stderr\": 0.036942843353378,\n \"acc_norm\": 0.18181818181818182,\n \"acc_norm_stderr\": 0.036942843353378\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.2816326530612245,\n \"acc_stderr\": 0.028795185574291286,\n \"acc_norm\": 0.2816326530612245,\n \"acc_norm_stderr\": 0.028795185574291286\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.22885572139303484,\n \"acc_stderr\": 0.029705284056772426,\n \"acc_norm\": 0.22885572139303484,\n \"acc_norm_stderr\": 0.029705284056772426\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.22,\n \"acc_stderr\": 0.041633319989322695,\n \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.041633319989322695\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.2289156626506024,\n \"acc_stderr\": 0.03270745277352477,\n \"acc_norm\": 0.2289156626506024,\n \"acc_norm_stderr\": 0.03270745277352477\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.23976608187134502,\n \"acc_stderr\": 0.03274485211946956,\n \"acc_norm\": 0.23976608187134502,\n \"acc_norm_stderr\": 0.03274485211946956\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2215422276621787,\n \"mc1_stderr\": 0.014537867601301145,\n \"mc2\": 0.4805606031451568,\n \"mc2_stderr\": 0.016999605402858272\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.47829518547750594,\n \"acc_stderr\": 0.01403923921648463\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n }\n}\n```", "repo_url": "https://huggingface.co/KnutJaegersberg/internlm-20b-llamafied", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2024_01_13T19_39_44.590825", "path": ["**/details_harness|arc:challenge|25_2024-01-13T19-39-44.590825.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-01-13T19-39-44.590825.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_01_13T19_39_44.590825", "path": ["**/details_harness|gsm8k|5_2024-01-13T19-39-44.590825.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-01-13T19-39-44.590825.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_01_13T19_39_44.590825", "path": ["**/details_harness|hellaswag|10_2024-01-13T19-39-44.590825.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-01-13T19-39-44.590825.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_01_13T19_39_44.590825", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-econometrics|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-global_facts|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T19-39-44.590825.parquet", 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["**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-13T19-39-44.590825.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-13T19-39-44.590825.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_statistics_5", "data_files": [{"split": "2024_01_13T19_39_44.590825", "path": ["**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-13T19-39-44.590825.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-13T19-39-44.590825.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_us_history_5", "data_files": [{"split": "2024_01_13T19_39_44.590825", "path": ["**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-13T19-39-44.590825.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-13T19-39-44.590825.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_world_history_5", "data_files": [{"split": "2024_01_13T19_39_44.590825", "path": ["**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T19-39-44.590825.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T19-39-44.590825.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_aging_5", "data_files": [{"split": "2024_01_13T19_39_44.590825", "path": ["**/details_harness|hendrycksTest-human_aging|5_2024-01-13T19-39-44.590825.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-human_aging|5_2024-01-13T19-39-44.590825.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_sexuality_5", "data_files": [{"split": "2024_01_13T19_39_44.590825", "path": ["**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T19-39-44.590825.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T19-39-44.590825.parquet"]}]}, {"config_name": "harness_hendrycksTest_international_law_5", "data_files": [{"split": "2024_01_13T19_39_44.590825", "path": ["**/details_harness|hendrycksTest-international_law|5_2024-01-13T19-39-44.590825.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-international_law|5_2024-01-13T19-39-44.590825.parquet"]}]}, {"config_name": "harness_hendrycksTest_jurisprudence_5", "data_files": [{"split": "2024_01_13T19_39_44.590825", "path": ["**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T19-39-44.590825.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T19-39-44.590825.parquet"]}]}, {"config_name": "harness_hendrycksTest_logical_fallacies_5", "data_files": [{"split": "2024_01_13T19_39_44.590825", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T19-39-44.590825.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T19-39-44.590825.parquet"]}]}, {"config_name": "harness_hendrycksTest_machine_learning_5", "data_files": [{"split": "2024_01_13T19_39_44.590825", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T19-39-44.590825.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T19-39-44.590825.parquet"]}]}, {"config_name": "harness_hendrycksTest_management_5", "data_files": [{"split": "2024_01_13T19_39_44.590825", "path": ["**/details_harness|hendrycksTest-management|5_2024-01-13T19-39-44.590825.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-management|5_2024-01-13T19-39-44.590825.parquet"]}]}, {"config_name": "harness_hendrycksTest_marketing_5", "data_files": [{"split": "2024_01_13T19_39_44.590825", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-13T19-39-44.590825.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-13T19-39-44.590825.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2024_01_13T19_39_44.590825", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T19-39-44.590825.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T19-39-44.590825.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2024_01_13T19_39_44.590825", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T19-39-44.590825.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T19-39-44.590825.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2024_01_13T19_39_44.590825", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T19-39-44.590825.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T19-39-44.590825.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2024_01_13T19_39_44.590825", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T19-39-44.590825.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T19-39-44.590825.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2024_01_13T19_39_44.590825", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T19-39-44.590825.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T19-39-44.590825.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2024_01_13T19_39_44.590825", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T19-39-44.590825.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T19-39-44.590825.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2024_01_13T19_39_44.590825", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T19-39-44.590825.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T19-39-44.590825.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2024_01_13T19_39_44.590825", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T19-39-44.590825.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T19-39-44.590825.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2024_01_13T19_39_44.590825", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T19-39-44.590825.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T19-39-44.590825.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_01_13T19_39_44.590825", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T19-39-44.590825.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T19-39-44.590825.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_01_13T19_39_44.590825", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T19-39-44.590825.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T19-39-44.590825.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_01_13T19_39_44.590825", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T19-39-44.590825.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T19-39-44.590825.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_01_13T19_39_44.590825", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T19-39-44.590825.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T19-39-44.590825.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_01_13T19_39_44.590825", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T19-39-44.590825.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T19-39-44.590825.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_01_13T19_39_44.590825", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T19-39-44.590825.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T19-39-44.590825.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_13T19_39_44.590825", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T19-39-44.590825.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T19-39-44.590825.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_13T19_39_44.590825", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T19-39-44.590825.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T19-39-44.590825.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_13T19_39_44.590825", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T19-39-44.590825.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T19-39-44.590825.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T19_39_44.590825", "path": ["**/details_harness|winogrande|5_2024-01-13T19-39-44.590825.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T19-39-44.590825.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T19_39_44.590825", "path": ["results_2024-01-13T19-39-44.590825.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T19-39-44.590825.parquet"]}]}]} | 2024-01-13T19:42:21+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of KnutJaegersberg/internlm-20b-llamafied
Dataset automatically created during the evaluation run of model KnutJaegersberg/internlm-20b-llamafied on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-13T19:39:44.590825(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of KnutJaegersberg/internlm-20b-llamafied\n\n\n\nDataset automatically created during the evaluation run of model KnutJaegersberg/internlm-20b-llamafied on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T19:39:44.590825(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of KnutJaegersberg/internlm-20b-llamafied\n\n\n\nDataset automatically created during the evaluation run of model KnutJaegersberg/internlm-20b-llamafied on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T19:39:44.590825(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
a32266c24a2ec57996f9d9da4e209b3260e4c3b5 |
# Dataset of akatsuki/暁/晓 (Azur Lane)
This is the dataset of akatsuki/暁/晓 (Azur Lane), containing 17 images and their tags.
The core tags of this character are `black_hair, long_hair, ponytail, bangs, red_eyes, hair_between_eyes, breasts, eyepatch, high_ponytail, horns`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 17 | 13.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/akatsuki_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 17 | 10.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/akatsuki_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 40 | 19.30 MiB | [Download](https://huggingface.co/datasets/CyberHarem/akatsuki_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 17 | 12.87 MiB | [Download](https://huggingface.co/datasets/CyberHarem/akatsuki_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 40 | 22.53 MiB | [Download](https://huggingface.co/datasets/CyberHarem/akatsuki_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/akatsuki_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 13 |  |  |  |  |  | 1girl, looking_at_viewer, solo, mask, scarf, simple_background, white_background, elbow_gloves, fingerless_gloves, full_body, midriff, ninja, weapon |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | mask | scarf | simple_background | white_background | elbow_gloves | fingerless_gloves | full_body | midriff | ninja | weapon |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:-------|:--------|:--------------------|:-------------------|:---------------|:--------------------|:------------|:----------|:--------|:---------|
| 0 | 13 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/akatsuki_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T19:42:29+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T19:52:34+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of akatsuki/暁/晓 (Azur Lane)
===================================
This is the dataset of akatsuki/暁/晓 (Azur Lane), containing 17 images and their tags.
The core tags of this character are 'black\_hair, long\_hair, ponytail, bangs, red\_eyes, hair\_between\_eyes, breasts, eyepatch, high\_ponytail, horns', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
008fd30fa61d32baa2411a1f5817578f38d711a2 |
# Dataset of duca_degli_abruzzi/ドゥーカ・デッリ・アブルッツィ/阿布鲁齐公爵 (Azur Lane)
This is the dataset of duca_degli_abruzzi/ドゥーカ・デッリ・アブルッツィ/阿布鲁齐公爵 (Azur Lane), containing 83 images and their tags.
The core tags of this character are `breasts, red_eyes, large_breasts, bangs, halo, long_hair, pink_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 83 | 149.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/duca_degli_abruzzi_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 83 | 67.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/duca_degli_abruzzi_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 216 | 150.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/duca_degli_abruzzi_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 83 | 124.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/duca_degli_abruzzi_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 216 | 232.11 MiB | [Download](https://huggingface.co/datasets/CyberHarem/duca_degli_abruzzi_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/duca_degli_abruzzi_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 5 |  |  |  |  |  | epaulettes, long_sleeves, looking_at_viewer, rabbit_ears, single_mechanical_arm, 1girl, black_gloves, black_jacket, cleavage, fake_animal_ears, holding, navel, thighs, black_panties, garter_straps, parted_lips, red_nails, stomach, tail, white_background, 2girls, arm_up, black_bowtie, black_footwear, brown_hair, fishnet_thighhighs, frills, full_body, hair_between_eyes, high_heels, highleg, jewelry, kneeling, red_hair, simple_background, skindentation, solo_focus, standing |
| 1 | 5 |  |  |  |  |  | 1girl, bare_shoulders, bowtie, detached_collar, playboy_bunny, rabbit_ears, wrist_cuffs, cleavage, looking_at_viewer, red_nails, side_drill, side_ponytail, solo, white_background, earrings, fake_animal_ears, fishnet_pantyhose, nail_polish, simple_background, sitting, bare_legs, black_bow, black_leotard, brown_hair, drinking_glass, holding_tray, parted_lips, single_elbow_glove, single_glove, skindentation |
| 2 | 15 |  |  |  |  |  | 1girl, cleavage, looking_at_viewer, solo, single_mechanical_arm, closed_mouth, official_alternate_costume, prosthetic_arm, smile, bare_shoulders, collarbone, sitting, thighs, black_one-piece_swimsuit, jewelry |
| 3 | 7 |  |  |  |  |  | 1girl, looking_at_viewer, solo, blue_sky, navel, outdoors, prosthetic_arm, single_mechanical_arm, thighs, water, white_bikini, collarbone, side-tie_bikini_bottom, cleavage, cloud, day, nail_polish, parted_lips, smile, standing, wet, white_choker, arm_up, red_nails, wading |
| 4 | 9 |  |  |  |  |  | 1girl, black_gloves, italian_flag, pantyhose, red_necktie, red_skirt, single_mechanical_arm, solo, dress, drill_locks, sideboob, single_elbow_glove, standing, brown_hair, green_cape, holding, thigh_strap, prosthetic_arm, simple_background, white_background, drill_hair, looking_at_viewer, armpits, high_heels, medium_hair |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | epaulettes | long_sleeves | looking_at_viewer | rabbit_ears | single_mechanical_arm | 1girl | black_gloves | black_jacket | cleavage | fake_animal_ears | holding | navel | thighs | black_panties | garter_straps | parted_lips | red_nails | stomach | tail | white_background | 2girls | arm_up | black_bowtie | black_footwear | brown_hair | fishnet_thighhighs | frills | full_body | hair_between_eyes | high_heels | highleg | jewelry | kneeling | red_hair | simple_background | skindentation | solo_focus | standing | bare_shoulders | bowtie | detached_collar | playboy_bunny | wrist_cuffs | side_drill | side_ponytail | solo | earrings | fishnet_pantyhose | nail_polish | sitting | bare_legs | black_bow | black_leotard | drinking_glass | holding_tray | single_elbow_glove | single_glove | closed_mouth | official_alternate_costume | prosthetic_arm | smile | collarbone | black_one-piece_swimsuit | blue_sky | outdoors | water | white_bikini | side-tie_bikini_bottom | cloud | day | wet | white_choker | wading | italian_flag | pantyhose | red_necktie | red_skirt | dress | drill_locks | sideboob | green_cape | thigh_strap | drill_hair | armpits | medium_hair |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------|:---------------|:--------------------|:--------------|:------------------------|:--------|:---------------|:---------------|:-----------|:-------------------|:----------|:--------|:---------|:----------------|:----------------|:--------------|:------------|:----------|:-------|:-------------------|:---------|:---------|:---------------|:-----------------|:-------------|:---------------------|:---------|:------------|:--------------------|:-------------|:----------|:----------|:-----------|:-----------|:--------------------|:----------------|:-------------|:-----------|:-----------------|:---------|:------------------|:----------------|:--------------|:-------------|:----------------|:-------|:-----------|:--------------------|:--------------|:----------|:------------|:------------|:----------------|:-----------------|:---------------|:---------------------|:---------------|:---------------|:-----------------------------|:-----------------|:--------|:-------------|:---------------------------|:-----------|:-----------|:--------|:---------------|:-------------------------|:--------|:------|:------|:---------------|:---------|:---------------|:------------|:--------------|:------------|:--------|:--------------|:-----------|:-------------|:--------------|:-------------|:----------|:--------------|
| 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | | | X | X | | X | | | X | X | | | | | | X | X | | | X | | | | | X | | | | | | | | | | X | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 15 |  |  |  |  |  | | | X | | X | X | | | X | | | | X | | | | | | | | | | | | | | | | | | | X | | | | | | | X | | | | | | | X | | | | X | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 7 |  |  |  |  |  | | | X | | X | X | | | X | | | X | X | | | X | X | | | | | X | | | | | | | | | | | | | | | | X | | | | | | | | X | | | X | | | | | | | | | | | X | X | X | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | |
| 4 | 9 |  |  |  |  |  | | | X | | X | X | X | | | | X | | | | | | | | | X | | | | | X | | | | | X | | | | | X | | | X | | | | | | | | X | | | | | | | | | | X | | | | X | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/duca_degli_abruzzi_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T19:42:38+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T20:12:49+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of duca\_degli\_abruzzi/ドゥーカ・デッリ・アブルッツィ/阿布鲁齐公爵 (Azur Lane)
==================================================================
This is the dataset of duca\_degli\_abruzzi/ドゥーカ・デッリ・アブルッツィ/阿布鲁齐公爵 (Azur Lane), containing 83 images and their tags.
The core tags of this character are 'breasts, red\_eyes, large\_breasts, bangs, halo, long\_hair, pink\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
bc019f2161b17717909c197a65c4f153a81ffaa2 |
# Dataset Card for Evaluation run of FelixChao/WizardDolphin-7B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [FelixChao/WizardDolphin-7B](https://huggingface.co/FelixChao/WizardDolphin-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_FelixChao__WizardDolphin-7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T19:47:12.026725](https://huggingface.co/datasets/open-llm-leaderboard/details_FelixChao__WizardDolphin-7B/blob/main/results_2024-01-13T19-47-12.026725.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6312916506690491,
"acc_stderr": 0.0324258954325278,
"acc_norm": 0.6317815176886508,
"acc_norm_stderr": 0.03308456506657342,
"mc1": 0.42105263157894735,
"mc1_stderr": 0.017283936248136487,
"mc2": 0.5927990044155668,
"mc2_stderr": 0.01547758043423419
},
"harness|arc:challenge|25": {
"acc": 0.6203071672354948,
"acc_stderr": 0.014182119866974872,
"acc_norm": 0.6467576791808873,
"acc_norm_stderr": 0.013967822714840055
},
"harness|hellaswag|10": {
"acc": 0.6707827126070504,
"acc_stderr": 0.004689685978155169,
"acc_norm": 0.8585939055964947,
"acc_norm_stderr": 0.003477278544493499
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.37,
"acc_stderr": 0.04852365870939098,
"acc_norm": 0.37,
"acc_norm_stderr": 0.04852365870939098
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6370370370370371,
"acc_stderr": 0.04153948404742398,
"acc_norm": 0.6370370370370371,
"acc_norm_stderr": 0.04153948404742398
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6973684210526315,
"acc_stderr": 0.03738520676119667,
"acc_norm": 0.6973684210526315,
"acc_norm_stderr": 0.03738520676119667
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.59,
"acc_stderr": 0.049431107042371025,
"acc_norm": 0.59,
"acc_norm_stderr": 0.049431107042371025
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6754716981132075,
"acc_stderr": 0.02881561571343211,
"acc_norm": 0.6754716981132075,
"acc_norm_stderr": 0.02881561571343211
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7430555555555556,
"acc_stderr": 0.03653946969442099,
"acc_norm": 0.7430555555555556,
"acc_norm_stderr": 0.03653946969442099
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.38,
"acc_stderr": 0.04878317312145632,
"acc_norm": 0.38,
"acc_norm_stderr": 0.04878317312145632
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.56,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.56,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.29,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.29,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6069364161849711,
"acc_stderr": 0.037242495958177295,
"acc_norm": 0.6069364161849711,
"acc_norm_stderr": 0.037242495958177295
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4117647058823529,
"acc_stderr": 0.04897104952726366,
"acc_norm": 0.4117647058823529,
"acc_norm_stderr": 0.04897104952726366
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.77,
"acc_stderr": 0.042295258468165065,
"acc_norm": 0.77,
"acc_norm_stderr": 0.042295258468165065
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5191489361702127,
"acc_stderr": 0.03266204299064678,
"acc_norm": 0.5191489361702127,
"acc_norm_stderr": 0.03266204299064678
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.45614035087719296,
"acc_stderr": 0.046854730419077895,
"acc_norm": 0.45614035087719296,
"acc_norm_stderr": 0.046854730419077895
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5379310344827586,
"acc_stderr": 0.04154659671707548,
"acc_norm": 0.5379310344827586,
"acc_norm_stderr": 0.04154659671707548
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.42063492063492064,
"acc_stderr": 0.025424835086924003,
"acc_norm": 0.42063492063492064,
"acc_norm_stderr": 0.025424835086924003
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.3888888888888889,
"acc_stderr": 0.04360314860077459,
"acc_norm": 0.3888888888888889,
"acc_norm_stderr": 0.04360314860077459
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.35,
"acc_stderr": 0.047937248544110196,
"acc_norm": 0.35,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7677419354838709,
"acc_stderr": 0.024022256130308235,
"acc_norm": 0.7677419354838709,
"acc_norm_stderr": 0.024022256130308235
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5073891625615764,
"acc_stderr": 0.035176035403610105,
"acc_norm": 0.5073891625615764,
"acc_norm_stderr": 0.035176035403610105
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.7,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7575757575757576,
"acc_stderr": 0.03346409881055953,
"acc_norm": 0.7575757575757576,
"acc_norm_stderr": 0.03346409881055953
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7727272727272727,
"acc_stderr": 0.029857515673386417,
"acc_norm": 0.7727272727272727,
"acc_norm_stderr": 0.029857515673386417
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8756476683937824,
"acc_stderr": 0.02381447708659355,
"acc_norm": 0.8756476683937824,
"acc_norm_stderr": 0.02381447708659355
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6256410256410256,
"acc_stderr": 0.0245375915728305,
"acc_norm": 0.6256410256410256,
"acc_norm_stderr": 0.0245375915728305
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.32592592592592595,
"acc_stderr": 0.02857834836547308,
"acc_norm": 0.32592592592592595,
"acc_norm_stderr": 0.02857834836547308
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6428571428571429,
"acc_stderr": 0.031124619309328177,
"acc_norm": 0.6428571428571429,
"acc_norm_stderr": 0.031124619309328177
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.2913907284768212,
"acc_stderr": 0.037101857261199946,
"acc_norm": 0.2913907284768212,
"acc_norm_stderr": 0.037101857261199946
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8128440366972477,
"acc_stderr": 0.01672268452620014,
"acc_norm": 0.8128440366972477,
"acc_norm_stderr": 0.01672268452620014
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.4675925925925926,
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"harness|hendrycksTest-high_school_world_history|5": {
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"harness|hendrycksTest-sociology|5": {
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"harness|hendrycksTest-us_foreign_policy|5": {
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"harness|hendrycksTest-virology|5": {
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"harness|hendrycksTest-world_religions|5": {
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"harness|winogrande|5": {
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},
"harness|gsm8k|5": {
"acc": 0.6626231993934799,
"acc_stderr": 0.013023665136222088
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Annotation process
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#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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## Bias, Risks, and Limitations
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_FelixChao__WizardDolphin-7B | [
"region:us"
] | 2024-01-13T19:49:31+00:00 | {"pretty_name": "Evaluation run of FelixChao/WizardDolphin-7B", "dataset_summary": "Dataset automatically created during the evaluation run of model [FelixChao/WizardDolphin-7B](https://huggingface.co/FelixChao/WizardDolphin-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_FelixChao__WizardDolphin-7B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T19:47:12.026725](https://huggingface.co/datasets/open-llm-leaderboard/details_FelixChao__WizardDolphin-7B/blob/main/results_2024-01-13T19-47-12.026725.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6312916506690491,\n \"acc_stderr\": 0.0324258954325278,\n \"acc_norm\": 0.6317815176886508,\n \"acc_norm_stderr\": 0.03308456506657342,\n \"mc1\": 0.42105263157894735,\n \"mc1_stderr\": 0.017283936248136487,\n \"mc2\": 0.5927990044155668,\n \"mc2_stderr\": 0.01547758043423419\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6203071672354948,\n \"acc_stderr\": 0.014182119866974872,\n \"acc_norm\": 0.6467576791808873,\n \"acc_norm_stderr\": 0.013967822714840055\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6707827126070504,\n \"acc_stderr\": 0.004689685978155169,\n \"acc_norm\": 0.8585939055964947,\n \"acc_norm_stderr\": 0.003477278544493499\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939098,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939098\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6370370370370371,\n \"acc_stderr\": 0.04153948404742398,\n \"acc_norm\": 0.6370370370370371,\n \"acc_norm_stderr\": 0.04153948404742398\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6973684210526315,\n \"acc_stderr\": 0.03738520676119667,\n \"acc_norm\": 0.6973684210526315,\n \"acc_norm_stderr\": 0.03738520676119667\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.59,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6754716981132075,\n \"acc_stderr\": 0.02881561571343211,\n \"acc_norm\": 0.6754716981132075,\n \"acc_norm_stderr\": 0.02881561571343211\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7430555555555556,\n \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.7430555555555556,\n \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.56,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6069364161849711,\n \"acc_stderr\": 0.037242495958177295,\n \"acc_norm\": 0.6069364161849711,\n \"acc_norm_stderr\": 0.037242495958177295\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.04897104952726366,\n \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.04897104952726366\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.77,\n \"acc_stderr\": 0.042295258468165065,\n \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.042295258468165065\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5191489361702127,\n \"acc_stderr\": 0.03266204299064678,\n \"acc_norm\": 0.5191489361702127,\n \"acc_norm_stderr\": 0.03266204299064678\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.45614035087719296,\n \"acc_stderr\": 0.046854730419077895,\n \"acc_norm\": 0.45614035087719296,\n \"acc_norm_stderr\": 0.046854730419077895\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5379310344827586,\n \"acc_stderr\": 0.04154659671707548,\n \"acc_norm\": 0.5379310344827586,\n \"acc_norm_stderr\": 0.04154659671707548\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.42063492063492064,\n \"acc_stderr\": 0.025424835086924003,\n \"acc_norm\": 0.42063492063492064,\n \"acc_norm_stderr\": 0.025424835086924003\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3888888888888889,\n \"acc_stderr\": 0.04360314860077459,\n \"acc_norm\": 0.3888888888888889,\n \"acc_norm_stderr\": 0.04360314860077459\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7677419354838709,\n \"acc_stderr\": 0.024022256130308235,\n \"acc_norm\": 0.7677419354838709,\n \"acc_norm_stderr\": 0.024022256130308235\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5073891625615764,\n \"acc_stderr\": 0.035176035403610105,\n \"acc_norm\": 0.5073891625615764,\n \"acc_norm_stderr\": 0.035176035403610105\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7575757575757576,\n \"acc_stderr\": 0.03346409881055953,\n \"acc_norm\": 0.7575757575757576,\n \"acc_norm_stderr\": 0.03346409881055953\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7727272727272727,\n \"acc_stderr\": 0.029857515673386417,\n \"acc_norm\": 0.7727272727272727,\n \"acc_norm_stderr\": 0.029857515673386417\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8756476683937824,\n \"acc_stderr\": 0.02381447708659355,\n \"acc_norm\": 0.8756476683937824,\n \"acc_norm_stderr\": 0.02381447708659355\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6256410256410256,\n \"acc_stderr\": 0.0245375915728305,\n \"acc_norm\": 0.6256410256410256,\n \"acc_norm_stderr\": 0.0245375915728305\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.32592592592592595,\n \"acc_stderr\": 0.02857834836547308,\n \"acc_norm\": 0.32592592592592595,\n \"acc_norm_stderr\": 0.02857834836547308\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6428571428571429,\n \"acc_stderr\": 0.031124619309328177,\n \"acc_norm\": 0.6428571428571429,\n \"acc_norm_stderr\": 0.031124619309328177\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.2913907284768212,\n \"acc_stderr\": 0.037101857261199946,\n \"acc_norm\": 0.2913907284768212,\n \"acc_norm_stderr\": 0.037101857261199946\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8128440366972477,\n \"acc_stderr\": 0.01672268452620014,\n \"acc_norm\": 0.8128440366972477,\n \"acc_norm_stderr\": 0.01672268452620014\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.4675925925925926,\n \"acc_stderr\": 0.03402801581358966,\n \"acc_norm\": 0.4675925925925926,\n \"acc_norm_stderr\": 0.03402801581358966\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8186274509803921,\n \"acc_stderr\": 0.027044621719474082,\n \"acc_norm\": 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["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T19-47-12.026725.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T19-47-12.026725.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_13T19_47_12.026725", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T19-47-12.026725.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T19-47-12.026725.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_13T19_47_12.026725", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T19-47-12.026725.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T19-47-12.026725.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_13T19_47_12.026725", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T19-47-12.026725.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T19-47-12.026725.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T19_47_12.026725", "path": ["**/details_harness|winogrande|5_2024-01-13T19-47-12.026725.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T19-47-12.026725.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T19_47_12.026725", "path": ["results_2024-01-13T19-47-12.026725.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T19-47-12.026725.parquet"]}]}]} | 2024-01-13T19:49:52+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of FelixChao/WizardDolphin-7B
Dataset automatically created during the evaluation run of model FelixChao/WizardDolphin-7B on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-13T19:47:12.026725(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of FelixChao/WizardDolphin-7B\n\n\n\nDataset automatically created during the evaluation run of model FelixChao/WizardDolphin-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T19:47:12.026725(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of FelixChao/WizardDolphin-7B\n\n\n\nDataset automatically created during the evaluation run of model FelixChao/WizardDolphin-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T19:47:12.026725(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
ebd3abaf74f0b5f651e7ade146db34dc1aaf1c17 |
# Dataset Card for Evaluation run of EmbeddedLLM/Mistral-7B-Merge-14-v0.5
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [EmbeddedLLM/Mistral-7B-Merge-14-v0.5](https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.5) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_EmbeddedLLM__Mistral-7B-Merge-14-v0.5",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T20:06:30.676415](https://huggingface.co/datasets/open-llm-leaderboard/details_EmbeddedLLM__Mistral-7B-Merge-14-v0.5/blob/main/results_2024-01-13T20-06-30.676415.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6602010739871594,
"acc_stderr": 0.031725005763123176,
"acc_norm": 0.6605298747796313,
"acc_norm_stderr": 0.032373589088471474,
"mc1": 0.4259485924112607,
"mc1_stderr": 0.017310471904076544,
"mc2": 0.5911635736956555,
"mc2_stderr": 0.015563030300185875
},
"harness|arc:challenge|25": {
"acc": 0.6501706484641638,
"acc_stderr": 0.013936809212158289,
"acc_norm": 0.6868600682593856,
"acc_norm_stderr": 0.013552671543623494
},
"harness|hellaswag|10": {
"acc": 0.6836287592113125,
"acc_stderr": 0.0046410920014252925,
"acc_norm": 0.8644692292372037,
"acc_norm_stderr": 0.003415900722381889
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252606,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252606
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6666666666666666,
"acc_stderr": 0.04072314811876837,
"acc_norm": 0.6666666666666666,
"acc_norm_stderr": 0.04072314811876837
},
"harness|hendrycksTest-astronomy|5": {
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"acc_stderr": 0.03715062154998904,
"acc_norm": 0.7039473684210527,
"acc_norm_stderr": 0.03715062154998904
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.61,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.61,
"acc_norm_stderr": 0.04902071300001975
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.720754716981132,
"acc_stderr": 0.027611163402399715,
"acc_norm": 0.720754716981132,
"acc_norm_stderr": 0.027611163402399715
},
"harness|hendrycksTest-college_biology|5": {
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"acc_stderr": 0.03437079344106135,
"acc_norm": 0.7847222222222222,
"acc_norm_stderr": 0.03437079344106135
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.47,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.47,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.52,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.52,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.29,
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"acc_norm": 0.29,
"acc_norm_stderr": 0.04560480215720684
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.7052023121387283,
"acc_stderr": 0.034765996075164785,
"acc_norm": 0.7052023121387283,
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},
"harness|hendrycksTest-college_physics|5": {
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"acc_norm_stderr": 0.048971049527263666
},
"harness|hendrycksTest-computer_security|5": {
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"acc_norm": 0.77,
"acc_norm_stderr": 0.04229525846816506
},
"harness|hendrycksTest-conceptual_physics|5": {
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"acc_norm_stderr": 0.03184389265339526
},
"harness|hendrycksTest-econometrics|5": {
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"acc_norm": 0.5175438596491229,
"acc_norm_stderr": 0.04700708033551038
},
"harness|hendrycksTest-electrical_engineering|5": {
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"acc_norm": 0.5724137931034483,
"acc_norm_stderr": 0.04122737111370332
},
"harness|hendrycksTest-elementary_mathematics|5": {
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},
"harness|hendrycksTest-formal_logic|5": {
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"acc_norm": 0.47619047619047616,
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},
"harness|hendrycksTest-global_facts|5": {
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},
"harness|hendrycksTest-high_school_biology|5": {
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},
"harness|hendrycksTest-high_school_chemistry|5": {
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"harness|hendrycksTest-high_school_computer_science|5": {
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},
"harness|hendrycksTest-high_school_european_history|5": {
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},
"harness|hendrycksTest-high_school_geography|5": {
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},
"harness|hendrycksTest-high_school_government_and_politics|5": {
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},
"harness|hendrycksTest-high_school_mathematics|5": {
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},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.7058823529411765,
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"acc_norm": 0.7058823529411765,
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},
"harness|hendrycksTest-high_school_physics|5": {
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},
"harness|hendrycksTest-high_school_psychology|5": {
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"acc_norm_stderr": 0.01555580271359017
},
"harness|hendrycksTest-high_school_statistics|5": {
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},
"harness|hendrycksTest-high_school_us_history|5": {
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},
"harness|hendrycksTest-high_school_world_history|5": {
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},
"harness|hendrycksTest-logical_fallacies|5": {
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},
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},
"harness|hendrycksTest-marketing|5": {
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},
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},
"harness|hendrycksTest-moral_disputes|5": {
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},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.3575418994413408,
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},
"harness|hendrycksTest-nutrition|5": {
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"harness|hendrycksTest-prehistory|5": {
"acc": 0.7530864197530864,
"acc_stderr": 0.023993501709042117,
"acc_norm": 0.7530864197530864,
"acc_norm_stderr": 0.023993501709042117
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.5070921985815603,
"acc_stderr": 0.02982449855912901,
"acc_norm": 0.5070921985815603,
"acc_norm_stderr": 0.02982449855912901
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4791395045632334,
"acc_stderr": 0.012759117066518015,
"acc_norm": 0.4791395045632334,
"acc_norm_stderr": 0.012759117066518015
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.7058823529411765,
"acc_stderr": 0.02767846864214472,
"acc_norm": 0.7058823529411765,
"acc_norm_stderr": 0.02767846864214472
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6862745098039216,
"acc_stderr": 0.018771683893528183,
"acc_norm": 0.6862745098039216,
"acc_norm_stderr": 0.018771683893528183
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6727272727272727,
"acc_stderr": 0.0449429086625209,
"acc_norm": 0.6727272727272727,
"acc_norm_stderr": 0.0449429086625209
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7306122448979592,
"acc_stderr": 0.02840125202902294,
"acc_norm": 0.7306122448979592,
"acc_norm_stderr": 0.02840125202902294
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8507462686567164,
"acc_stderr": 0.02519692987482706,
"acc_norm": 0.8507462686567164,
"acc_norm_stderr": 0.02519692987482706
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.86,
"acc_stderr": 0.0348735088019777,
"acc_norm": 0.86,
"acc_norm_stderr": 0.0348735088019777
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5301204819277109,
"acc_stderr": 0.03885425420866767,
"acc_norm": 0.5301204819277109,
"acc_norm_stderr": 0.03885425420866767
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.847953216374269,
"acc_stderr": 0.027539122889061452,
"acc_norm": 0.847953216374269,
"acc_norm_stderr": 0.027539122889061452
},
"harness|truthfulqa:mc|0": {
"mc1": 0.4259485924112607,
"mc1_stderr": 0.017310471904076544,
"mc2": 0.5911635736956555,
"mc2_stderr": 0.015563030300185875
},
"harness|winogrande|5": {
"acc": 0.8066298342541437,
"acc_stderr": 0.01109979664592053
},
"harness|gsm8k|5": {
"acc": 0.7119029567854435,
"acc_stderr": 0.012474469737197923
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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### Direct Use
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### Out-of-Scope Use
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## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Annotation process
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#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_EmbeddedLLM__Mistral-7B-Merge-14-v0.5 | [
"region:us"
] | 2024-01-13T20:08:58+00:00 | {"pretty_name": "Evaluation run of EmbeddedLLM/Mistral-7B-Merge-14-v0.5", "dataset_summary": "Dataset automatically created during the evaluation run of model [EmbeddedLLM/Mistral-7B-Merge-14-v0.5](https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.5) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_EmbeddedLLM__Mistral-7B-Merge-14-v0.5\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T20:06:30.676415](https://huggingface.co/datasets/open-llm-leaderboard/details_EmbeddedLLM__Mistral-7B-Merge-14-v0.5/blob/main/results_2024-01-13T20-06-30.676415.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6602010739871594,\n \"acc_stderr\": 0.031725005763123176,\n \"acc_norm\": 0.6605298747796313,\n \"acc_norm_stderr\": 0.032373589088471474,\n \"mc1\": 0.4259485924112607,\n \"mc1_stderr\": 0.017310471904076544,\n \"mc2\": 0.5911635736956555,\n \"mc2_stderr\": 0.015563030300185875\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6501706484641638,\n \"acc_stderr\": 0.013936809212158289,\n \"acc_norm\": 0.6868600682593856,\n \"acc_norm_stderr\": 0.013552671543623494\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6836287592113125,\n \"acc_stderr\": 0.0046410920014252925,\n \"acc_norm\": 0.8644692292372037,\n \"acc_norm_stderr\": 0.003415900722381889\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.04072314811876837,\n \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.04072314811876837\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.7039473684210527,\n \"acc_stderr\": 0.03715062154998904,\n \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.03715062154998904\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.720754716981132,\n \"acc_stderr\": 0.027611163402399715,\n \"acc_norm\": 0.720754716981132,\n \"acc_norm_stderr\": 0.027611163402399715\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7847222222222222,\n \"acc_stderr\": 0.03437079344106135,\n \"acc_norm\": 0.7847222222222222,\n \"acc_norm_stderr\": 0.03437079344106135\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7052023121387283,\n \"acc_stderr\": 0.034765996075164785,\n \"acc_norm\": 0.7052023121387283,\n \"acc_norm_stderr\": 0.034765996075164785\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.048971049527263666,\n \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.048971049527263666\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.77,\n \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.6127659574468085,\n \"acc_stderr\": 0.03184389265339526,\n \"acc_norm\": 0.6127659574468085,\n \"acc_norm_stderr\": 0.03184389265339526\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5175438596491229,\n \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.5175438596491229,\n \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5724137931034483,\n \"acc_stderr\": 0.04122737111370332,\n \"acc_norm\": 0.5724137931034483,\n \"acc_norm_stderr\": 0.04122737111370332\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.41005291005291006,\n \"acc_stderr\": 0.025331202438944427,\n \"acc_norm\": 0.41005291005291006,\n \"acc_norm_stderr\": 0.025331202438944427\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.47619047619047616,\n \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.47619047619047616,\n \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7967741935483871,\n \"acc_stderr\": 0.022891687984554956,\n \"acc_norm\": 0.7967741935483871,\n \"acc_norm_stderr\": 0.022891687984554956\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5073891625615764,\n \"acc_stderr\": 0.035176035403610105,\n \"acc_norm\": 0.5073891625615764,\n \"acc_norm_stderr\": 0.035176035403610105\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.031922715695483,\n \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.031922715695483\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7929292929292929,\n \"acc_stderr\": 0.028869778460267045,\n \"acc_norm\": 0.7929292929292929,\n \"acc_norm_stderr\": 0.028869778460267045\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.02150024957603348,\n \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.02150024957603348\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6717948717948717,\n \"acc_stderr\": 0.02380763319865727,\n \"acc_norm\": 0.6717948717948717,\n \"acc_norm_stderr\": 0.02380763319865727\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.34814814814814815,\n \"acc_stderr\": 0.029045600290616255,\n \"acc_norm\": 0.34814814814814815,\n \"acc_norm_stderr\": 0.029045600290616255\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.7058823529411765,\n \"acc_stderr\": 0.029597329730978086,\n \"acc_norm\": 0.7058823529411765,\n \"acc_norm_stderr\": 0.029597329730978086\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.36423841059602646,\n \"acc_stderr\": 0.03929111781242742,\n \"acc_norm\": 0.36423841059602646,\n \"acc_norm_stderr\": 0.03929111781242742\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8440366972477065,\n \"acc_stderr\": 0.01555580271359017,\n \"acc_norm\": 0.8440366972477065,\n \"acc_norm_stderr\": 0.01555580271359017\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5138888888888888,\n \"acc_stderr\": 0.03408655867977749,\n \"acc_norm\": 0.5138888888888888,\n \"acc_norm_stderr\": 0.03408655867977749\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8431372549019608,\n \"acc_stderr\": 0.02552472232455335,\n \"acc_norm\": 0.8431372549019608,\n \"acc_norm_stderr\": 0.02552472232455335\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.8227848101265823,\n \"acc_stderr\": 0.024856364184503228,\n \"acc_norm\": 0.8227848101265823,\n \"acc_norm_stderr\": 0.024856364184503228\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.695067264573991,\n \"acc_stderr\": 0.030898610882477515,\n \"acc_norm\": 0.695067264573991,\n \"acc_norm_stderr\": 0.030898610882477515\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.034981493854624734,\n \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.034981493854624734\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.8016528925619835,\n \"acc_stderr\": 0.03640118271990947,\n \"acc_norm\": 0.8016528925619835,\n \"acc_norm_stderr\": 0.03640118271990947\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8055555555555556,\n \"acc_stderr\": 0.03826076324884866,\n \"acc_norm\": 0.8055555555555556,\n \"acc_norm_stderr\": 0.03826076324884866\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7852760736196319,\n \"acc_stderr\": 0.03226219377286775,\n \"acc_norm\": 0.7852760736196319,\n \"acc_norm_stderr\": 0.03226219377286775\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.49107142857142855,\n \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.49107142857142855,\n \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.8252427184466019,\n \"acc_stderr\": 0.03760178006026621,\n \"acc_norm\": 0.8252427184466019,\n \"acc_norm_stderr\": 0.03760178006026621\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8846153846153846,\n \"acc_stderr\": 0.02093019318517933,\n \"acc_norm\": 0.8846153846153846,\n \"acc_norm_stderr\": 0.02093019318517933\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.74,\n \"acc_stderr\": 0.044084400227680794,\n \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.044084400227680794\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.842911877394636,\n \"acc_stderr\": 0.013012459322650717,\n \"acc_norm\": 0.842911877394636,\n \"acc_norm_stderr\": 0.013012459322650717\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7514450867052023,\n \"acc_stderr\": 0.023267528432100174,\n \"acc_norm\": 0.7514450867052023,\n \"acc_norm_stderr\": 0.023267528432100174\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3575418994413408,\n \"acc_stderr\": 0.01602939447489489,\n \"acc_norm\": 0.3575418994413408,\n \"acc_norm_stderr\": 0.01602939447489489\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.7320261437908496,\n \"acc_stderr\": 0.025360603796242553,\n \"acc_norm\": 0.7320261437908496,\n \"acc_norm_stderr\": 0.025360603796242553\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.729903536977492,\n \"acc_stderr\": 0.02521804037341063,\n \"acc_norm\": 0.729903536977492,\n \"acc_norm_stderr\": 0.02521804037341063\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.7530864197530864,\n \"acc_stderr\": 0.023993501709042117,\n \"acc_norm\": 0.7530864197530864,\n \"acc_norm_stderr\": 0.023993501709042117\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.5070921985815603,\n \"acc_stderr\": 0.02982449855912901,\n \"acc_norm\": 0.5070921985815603,\n \"acc_norm_stderr\": 0.02982449855912901\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4791395045632334,\n \"acc_stderr\": 0.012759117066518015,\n \"acc_norm\": 0.4791395045632334,\n \"acc_norm_stderr\": 0.012759117066518015\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.7058823529411765,\n \"acc_stderr\": 0.02767846864214472,\n \"acc_norm\": 0.7058823529411765,\n \"acc_norm_stderr\": 0.02767846864214472\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 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\"acc_stderr\": 0.03885425420866767,\n \"acc_norm\": 0.5301204819277109,\n \"acc_norm_stderr\": 0.03885425420866767\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.847953216374269,\n \"acc_stderr\": 0.027539122889061452,\n \"acc_norm\": 0.847953216374269,\n \"acc_norm_stderr\": 0.027539122889061452\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4259485924112607,\n \"mc1_stderr\": 0.017310471904076544,\n \"mc2\": 0.5911635736956555,\n \"mc2_stderr\": 0.015563030300185875\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.8066298342541437,\n \"acc_stderr\": 0.01109979664592053\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7119029567854435,\n \"acc_stderr\": 0.012474469737197923\n }\n}\n```", "repo_url": "https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.5", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": 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#region-us
|
# Dataset Card for Evaluation run of EmbeddedLLM/Mistral-7B-Merge-14-v0.5
Dataset automatically created during the evaluation run of model EmbeddedLLM/Mistral-7B-Merge-14-v0.5 on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-13T20:06:30.676415(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
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## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of EmbeddedLLM/Mistral-7B-Merge-14-v0.5\n\n\n\nDataset automatically created during the evaluation run of model EmbeddedLLM/Mistral-7B-Merge-14-v0.5 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T20:06:30.676415(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of EmbeddedLLM/Mistral-7B-Merge-14-v0.5\n\n\n\nDataset automatically created during the evaluation run of model EmbeddedLLM/Mistral-7B-Merge-14-v0.5 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T20:06:30.676415(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
c5c581546c579f6da6cee946175ef75e6ac6b239 |
# Dataset Card for Evaluation run of Josephgflowers/TinyLlama-3T-Cinder-v1.2
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Josephgflowers/TinyLlama-3T-Cinder-v1.2](https://huggingface.co/Josephgflowers/TinyLlama-3T-Cinder-v1.2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Josephgflowers__TinyLlama-3T-Cinder-v1.2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T20:09:00.533513](https://huggingface.co/datasets/open-llm-leaderboard/details_Josephgflowers__TinyLlama-3T-Cinder-v1.2/blob/main/results_2024-01-13T20-09-00.533513.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.26585070023086527,
"acc_stderr": 0.03098462149675041,
"acc_norm": 0.26789993726374506,
"acc_norm_stderr": 0.03179290872310728,
"mc1": 0.23011015911872704,
"mc1_stderr": 0.014734557959807763,
"mc2": 0.3678388335979262,
"mc2_stderr": 0.01427296099022099
},
"harness|arc:challenge|25": {
"acc": 0.310580204778157,
"acc_stderr": 0.013522292098053059,
"acc_norm": 0.3438566552901024,
"acc_norm_stderr": 0.013880644570156215
},
"harness|hellaswag|10": {
"acc": 0.4314877514439355,
"acc_stderr": 0.004942716091996078,
"acc_norm": 0.5651264688309102,
"acc_norm_stderr": 0.00494727245422622
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.29,
"acc_stderr": 0.04560480215720683,
"acc_norm": 0.29,
"acc_norm_stderr": 0.04560480215720683
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.2518518518518518,
"acc_stderr": 0.03749850709174021,
"acc_norm": 0.2518518518518518,
"acc_norm_stderr": 0.03749850709174021
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.2236842105263158,
"acc_stderr": 0.03391160934343602,
"acc_norm": 0.2236842105263158,
"acc_norm_stderr": 0.03391160934343602
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.21,
"acc_stderr": 0.040936018074033256,
"acc_norm": 0.21,
"acc_norm_stderr": 0.040936018074033256
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.2679245283018868,
"acc_stderr": 0.027257260322494845,
"acc_norm": 0.2679245283018868,
"acc_norm_stderr": 0.027257260322494845
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.3125,
"acc_stderr": 0.038760854559127644,
"acc_norm": 0.3125,
"acc_norm_stderr": 0.038760854559127644
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.2,
"acc_stderr": 0.04020151261036845,
"acc_norm": 0.2,
"acc_norm_stderr": 0.04020151261036845
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.32,
"acc_stderr": 0.04688261722621504,
"acc_norm": 0.32,
"acc_norm_stderr": 0.04688261722621504
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542127,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542127
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.2254335260115607,
"acc_stderr": 0.03186209851641144,
"acc_norm": 0.2254335260115607,
"acc_norm_stderr": 0.03186209851641144
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.19607843137254902,
"acc_stderr": 0.03950581861179961,
"acc_norm": 0.19607843137254902,
"acc_norm_stderr": 0.03950581861179961
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.25,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.25,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.20851063829787234,
"acc_stderr": 0.026556982117838742,
"acc_norm": 0.20851063829787234,
"acc_norm_stderr": 0.026556982117838742
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.2807017543859649,
"acc_stderr": 0.042270544512322004,
"acc_norm": 0.2807017543859649,
"acc_norm_stderr": 0.042270544512322004
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.2689655172413793,
"acc_stderr": 0.036951833116502325,
"acc_norm": 0.2689655172413793,
"acc_norm_stderr": 0.036951833116502325
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.2566137566137566,
"acc_stderr": 0.022494510767503154,
"acc_norm": 0.2566137566137566,
"acc_norm_stderr": 0.022494510767503154
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.2857142857142857,
"acc_stderr": 0.040406101782088394,
"acc_norm": 0.2857142857142857,
"acc_norm_stderr": 0.040406101782088394
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.14,
"acc_stderr": 0.034873508801977704,
"acc_norm": 0.14,
"acc_norm_stderr": 0.034873508801977704
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.267741935483871,
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"acc_norm": 0.267741935483871,
"acc_norm_stderr": 0.025189006660212385
},
"harness|hendrycksTest-high_school_chemistry|5": {
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"acc_norm": 0.24630541871921183,
"acc_norm_stderr": 0.030315099285617715
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.35,
"acc_stderr": 0.04793724854411019,
"acc_norm": 0.35,
"acc_norm_stderr": 0.04793724854411019
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.3212121212121212,
"acc_stderr": 0.036462049632538136,
"acc_norm": 0.3212121212121212,
"acc_norm_stderr": 0.036462049632538136
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.23737373737373738,
"acc_stderr": 0.0303137105381989,
"acc_norm": 0.23737373737373738,
"acc_norm_stderr": 0.0303137105381989
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.2849740932642487,
"acc_stderr": 0.03257714077709661,
"acc_norm": 0.2849740932642487,
"acc_norm_stderr": 0.03257714077709661
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.3384615384615385,
"acc_stderr": 0.023991500500313033,
"acc_norm": 0.3384615384615385,
"acc_norm_stderr": 0.023991500500313033
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.24814814814814815,
"acc_stderr": 0.026335739404055803,
"acc_norm": 0.24814814814814815,
"acc_norm_stderr": 0.026335739404055803
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.19327731092436976,
"acc_stderr": 0.02564947026588919,
"acc_norm": 0.19327731092436976,
"acc_norm_stderr": 0.02564947026588919
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.304635761589404,
"acc_stderr": 0.037579499229433426,
"acc_norm": 0.304635761589404,
"acc_norm_stderr": 0.037579499229433426
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.24036697247706423,
"acc_stderr": 0.01832060732096407,
"acc_norm": 0.24036697247706423,
"acc_norm_stderr": 0.01832060732096407
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.46296296296296297,
"acc_stderr": 0.03400603625538272,
"acc_norm": 0.46296296296296297,
"acc_norm_stderr": 0.03400603625538272
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.27941176470588236,
"acc_stderr": 0.03149328104507957,
"acc_norm": 0.27941176470588236,
"acc_norm_stderr": 0.03149328104507957
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.2911392405063291,
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"acc_norm": 0.2911392405063291,
"acc_norm_stderr": 0.02957160106575337
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.30493273542600896,
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"acc_norm": 0.30493273542600896,
"acc_norm_stderr": 0.030898610882477518
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.20610687022900764,
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"acc_norm_stderr": 0.03547771004159464
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.2396694214876033,
"acc_stderr": 0.03896878985070417,
"acc_norm": 0.2396694214876033,
"acc_norm_stderr": 0.03896878985070417
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.18518518518518517,
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"acc_norm": 0.18518518518518517,
"acc_norm_stderr": 0.03755265865037182
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.26993865030674846,
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},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.2767857142857143,
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"acc_norm": 0.2767857142857143,
"acc_norm_stderr": 0.04246624336697624
},
"harness|hendrycksTest-management|5": {
"acc": 0.24271844660194175,
"acc_stderr": 0.04245022486384495,
"acc_norm": 0.24271844660194175,
"acc_norm_stderr": 0.04245022486384495
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.23931623931623933,
"acc_stderr": 0.027951826808924333,
"acc_norm": 0.23931623931623933,
"acc_norm_stderr": 0.027951826808924333
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.22,
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"acc_norm": 0.22,
"acc_norm_stderr": 0.04163331998932269
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.2822477650063857,
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"acc_norm": 0.2822477650063857,
"acc_norm_stderr": 0.01609530296987857
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.23699421965317918,
"acc_stderr": 0.022894082489925992,
"acc_norm": 0.23699421965317918,
"acc_norm_stderr": 0.022894082489925992
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.24804469273743016,
"acc_stderr": 0.014444157808261436,
"acc_norm": 0.24804469273743016,
"acc_norm_stderr": 0.014444157808261436
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.26143790849673204,
"acc_stderr": 0.025160998214292456,
"acc_norm": 0.26143790849673204,
"acc_norm_stderr": 0.025160998214292456
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.2958199356913183,
"acc_stderr": 0.025922371788818798,
"acc_norm": 0.2958199356913183,
"acc_norm_stderr": 0.025922371788818798
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.2808641975308642,
"acc_stderr": 0.025006469755799208,
"acc_norm": 0.2808641975308642,
"acc_norm_stderr": 0.025006469755799208
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.24822695035460993,
"acc_stderr": 0.02577001564429039,
"acc_norm": 0.24822695035460993,
"acc_norm_stderr": 0.02577001564429039
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.24445893089960888,
"acc_stderr": 0.010976425013113912,
"acc_norm": 0.24445893089960888,
"acc_norm_stderr": 0.010976425013113912
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.3897058823529412,
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},
"harness|hendrycksTest-professional_psychology|5": {
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},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.22727272727272727,
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"acc_norm_stderr": 0.040139645540727735
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.22448979591836735,
"acc_stderr": 0.026711430555538422,
"acc_norm": 0.22448979591836735,
"acc_norm_stderr": 0.026711430555538422
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.24378109452736318,
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"acc_norm_stderr": 0.030360490154014652
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.22,
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"acc_norm": 0.22,
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},
"harness|hendrycksTest-virology|5": {
"acc": 0.2710843373493976,
"acc_stderr": 0.03460579907553027,
"acc_norm": 0.2710843373493976,
"acc_norm_stderr": 0.03460579907553027
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.2573099415204678,
"acc_stderr": 0.03352799844161865,
"acc_norm": 0.2573099415204678,
"acc_norm_stderr": 0.03352799844161865
},
"harness|truthfulqa:mc|0": {
"mc1": 0.23011015911872704,
"mc1_stderr": 0.014734557959807763,
"mc2": 0.3678388335979262,
"mc2_stderr": 0.01427296099022099
},
"harness|winogrande|5": {
"acc": 0.5769534333070244,
"acc_stderr": 0.013885055359056472
},
"harness|gsm8k|5": {
"acc": 0.000758150113722517,
"acc_stderr": 0.0007581501137225187
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
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## Glossary [optional]
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[More Information Needed] | open-llm-leaderboard/details_Josephgflowers__TinyLlama-3T-Cinder-v1.2 | [
"region:us"
] | 2024-01-13T20:10:51+00:00 | {"pretty_name": "Evaluation run of Josephgflowers/TinyLlama-3T-Cinder-v1.2", "dataset_summary": "Dataset automatically created during the evaluation run of model [Josephgflowers/TinyLlama-3T-Cinder-v1.2](https://huggingface.co/Josephgflowers/TinyLlama-3T-Cinder-v1.2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Josephgflowers__TinyLlama-3T-Cinder-v1.2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T20:09:00.533513](https://huggingface.co/datasets/open-llm-leaderboard/details_Josephgflowers__TinyLlama-3T-Cinder-v1.2/blob/main/results_2024-01-13T20-09-00.533513.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.26585070023086527,\n \"acc_stderr\": 0.03098462149675041,\n \"acc_norm\": 0.26789993726374506,\n \"acc_norm_stderr\": 0.03179290872310728,\n \"mc1\": 0.23011015911872704,\n \"mc1_stderr\": 0.014734557959807763,\n \"mc2\": 0.3678388335979262,\n \"mc2_stderr\": 0.01427296099022099\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.310580204778157,\n \"acc_stderr\": 0.013522292098053059,\n \"acc_norm\": 0.3438566552901024,\n \"acc_norm_stderr\": 0.013880644570156215\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.4314877514439355,\n \"acc_stderr\": 0.004942716091996078,\n \"acc_norm\": 0.5651264688309102,\n \"acc_norm_stderr\": 0.00494727245422622\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720683,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720683\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.2518518518518518,\n \"acc_stderr\": 0.03749850709174021,\n \"acc_norm\": 0.2518518518518518,\n \"acc_norm_stderr\": 0.03749850709174021\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.2236842105263158,\n \"acc_stderr\": 0.03391160934343602,\n \"acc_norm\": 0.2236842105263158,\n \"acc_norm_stderr\": 0.03391160934343602\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.2679245283018868,\n \"acc_stderr\": 0.027257260322494845,\n \"acc_norm\": 0.2679245283018868,\n \"acc_norm_stderr\": 0.027257260322494845\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.3125,\n \"acc_stderr\": 0.038760854559127644,\n \"acc_norm\": 0.3125,\n \"acc_norm_stderr\": 0.038760854559127644\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.2,\n \"acc_stderr\": 0.04020151261036845,\n \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.04020151261036845\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.2254335260115607,\n \"acc_stderr\": 0.03186209851641144,\n \"acc_norm\": 0.2254335260115607,\n \"acc_norm_stderr\": 0.03186209851641144\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.19607843137254902,\n \"acc_stderr\": 0.03950581861179961,\n \"acc_norm\": 0.19607843137254902,\n \"acc_norm_stderr\": 0.03950581861179961\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.20851063829787234,\n \"acc_stderr\": 0.026556982117838742,\n \"acc_norm\": 0.20851063829787234,\n \"acc_norm_stderr\": 0.026556982117838742\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2807017543859649,\n \"acc_stderr\": 0.042270544512322004,\n \"acc_norm\": 0.2807017543859649,\n \"acc_norm_stderr\": 0.042270544512322004\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.2689655172413793,\n \"acc_stderr\": 0.036951833116502325,\n \"acc_norm\": 0.2689655172413793,\n \"acc_norm_stderr\": 0.036951833116502325\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.2566137566137566,\n \"acc_stderr\": 0.022494510767503154,\n \"acc_norm\": 0.2566137566137566,\n \"acc_norm_stderr\": 0.022494510767503154\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2857142857142857,\n \"acc_stderr\": 0.040406101782088394,\n \"acc_norm\": 0.2857142857142857,\n \"acc_norm_stderr\": 0.040406101782088394\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.14,\n \"acc_stderr\": 0.034873508801977704,\n \"acc_norm\": 0.14,\n \"acc_norm_stderr\": 0.034873508801977704\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.267741935483871,\n \"acc_stderr\": 0.025189006660212385,\n \"acc_norm\": 0.267741935483871,\n \"acc_norm_stderr\": 0.025189006660212385\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.24630541871921183,\n \"acc_stderr\": 0.030315099285617715,\n \"acc_norm\": 0.24630541871921183,\n \"acc_norm_stderr\": 0.030315099285617715\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.04793724854411019,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.04793724854411019\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.3212121212121212,\n \"acc_stderr\": 0.036462049632538136,\n \"acc_norm\": 0.3212121212121212,\n \"acc_norm_stderr\": 0.036462049632538136\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.23737373737373738,\n \"acc_stderr\": 0.0303137105381989,\n \"acc_norm\": 0.23737373737373738,\n \"acc_norm_stderr\": 0.0303137105381989\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.2849740932642487,\n \"acc_stderr\": 0.03257714077709661,\n \"acc_norm\": 0.2849740932642487,\n \"acc_norm_stderr\": 0.03257714077709661\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.3384615384615385,\n \"acc_stderr\": 0.023991500500313033,\n \"acc_norm\": 0.3384615384615385,\n \"acc_norm_stderr\": 0.023991500500313033\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.24814814814814815,\n \"acc_stderr\": 0.026335739404055803,\n \"acc_norm\": 0.24814814814814815,\n \"acc_norm_stderr\": 0.026335739404055803\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.19327731092436976,\n \"acc_stderr\": 0.02564947026588919,\n \"acc_norm\": 0.19327731092436976,\n \"acc_norm_stderr\": 0.02564947026588919\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.304635761589404,\n \"acc_stderr\": 0.037579499229433426,\n \"acc_norm\": 0.304635761589404,\n \"acc_norm_stderr\": 0.037579499229433426\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.24036697247706423,\n \"acc_stderr\": 0.01832060732096407,\n \"acc_norm\": 0.24036697247706423,\n \"acc_norm_stderr\": 0.01832060732096407\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.46296296296296297,\n \"acc_stderr\": 0.03400603625538272,\n \"acc_norm\": 0.46296296296296297,\n \"acc_norm_stderr\": 0.03400603625538272\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.27941176470588236,\n \"acc_stderr\": 0.03149328104507957,\n \"acc_norm\": 0.27941176470588236,\n \"acc_norm_stderr\": 0.03149328104507957\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.2911392405063291,\n \"acc_stderr\": 0.02957160106575337,\n \"acc_norm\": 0.2911392405063291,\n \"acc_norm_stderr\": 0.02957160106575337\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.30493273542600896,\n \"acc_stderr\": 0.030898610882477518,\n \"acc_norm\": 0.30493273542600896,\n \"acc_norm_stderr\": 0.030898610882477518\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.20610687022900764,\n \"acc_stderr\": 0.03547771004159464,\n \"acc_norm\": 0.20610687022900764,\n \"acc_norm_stderr\": 0.03547771004159464\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.2396694214876033,\n \"acc_stderr\": 0.03896878985070417,\n \"acc_norm\": 0.2396694214876033,\n \"acc_norm_stderr\": 0.03896878985070417\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.18518518518518517,\n \"acc_stderr\": 0.03755265865037182,\n \"acc_norm\": 0.18518518518518517,\n \"acc_norm_stderr\": 0.03755265865037182\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.26993865030674846,\n \"acc_stderr\": 0.034878251684978906,\n \"acc_norm\": 0.26993865030674846,\n \"acc_norm_stderr\": 0.034878251684978906\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.2767857142857143,\n \"acc_stderr\": 0.04246624336697624,\n \"acc_norm\": 0.2767857142857143,\n \"acc_norm_stderr\": 0.04246624336697624\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.24271844660194175,\n \"acc_stderr\": 0.04245022486384495,\n \"acc_norm\": 0.24271844660194175,\n \"acc_norm_stderr\": 0.04245022486384495\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.23931623931623933,\n \"acc_stderr\": 0.027951826808924333,\n \"acc_norm\": 0.23931623931623933,\n \"acc_norm_stderr\": 0.027951826808924333\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.22,\n \"acc_stderr\": 0.04163331998932269,\n \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.04163331998932269\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.2822477650063857,\n \"acc_stderr\": 0.01609530296987857,\n \"acc_norm\": 0.2822477650063857,\n \"acc_norm_stderr\": 0.01609530296987857\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.23699421965317918,\n \"acc_stderr\": 0.022894082489925992,\n \"acc_norm\": 0.23699421965317918,\n \"acc_norm_stderr\": 0.022894082489925992\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.24804469273743016,\n \"acc_stderr\": 0.014444157808261436,\n \"acc_norm\": 0.24804469273743016,\n \"acc_norm_stderr\": 0.014444157808261436\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.26143790849673204,\n \"acc_stderr\": 0.025160998214292456,\n \"acc_norm\": 0.26143790849673204,\n \"acc_norm_stderr\": 0.025160998214292456\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2958199356913183,\n \"acc_stderr\": 0.025922371788818798,\n \"acc_norm\": 0.2958199356913183,\n \"acc_norm_stderr\": 0.025922371788818798\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.2808641975308642,\n \"acc_stderr\": 0.025006469755799208,\n \"acc_norm\": 0.2808641975308642,\n \"acc_norm_stderr\": 0.025006469755799208\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.24822695035460993,\n \"acc_stderr\": 0.02577001564429039,\n \"acc_norm\": 0.24822695035460993,\n \"acc_norm_stderr\": 0.02577001564429039\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.24445893089960888,\n \"acc_stderr\": 0.010976425013113912,\n \"acc_norm\": 0.24445893089960888,\n \"acc_norm_stderr\": 0.010976425013113912\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.3897058823529412,\n \"acc_stderr\": 0.02962466358115969,\n \"acc_norm\": 0.3897058823529412,\n \"acc_norm_stderr\": 0.02962466358115969\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.24673202614379086,\n \"acc_stderr\": 0.0174408203674025,\n \"acc_norm\": 0.24673202614379086,\n \"acc_norm_stderr\": 0.0174408203674025\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.22727272727272727,\n \"acc_stderr\": 0.040139645540727735,\n \"acc_norm\": 0.22727272727272727,\n \"acc_norm_stderr\": 0.040139645540727735\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.22448979591836735,\n \"acc_stderr\": 0.026711430555538422,\n \"acc_norm\": 0.22448979591836735,\n \"acc_norm_stderr\": 0.026711430555538422\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.24378109452736318,\n \"acc_stderr\": 0.030360490154014652,\n \"acc_norm\": 0.24378109452736318,\n \"acc_norm_stderr\": 0.030360490154014652\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.22,\n \"acc_stderr\": 0.04163331998932269,\n \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.04163331998932269\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.2710843373493976,\n \"acc_stderr\": 0.03460579907553027,\n \"acc_norm\": 0.2710843373493976,\n \"acc_norm_stderr\": 0.03460579907553027\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.2573099415204678,\n \"acc_stderr\": 0.03352799844161865,\n \"acc_norm\": 0.2573099415204678,\n \"acc_norm_stderr\": 0.03352799844161865\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.23011015911872704,\n \"mc1_stderr\": 0.014734557959807763,\n \"mc2\": 0.3678388335979262,\n \"mc2_stderr\": 0.01427296099022099\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5769534333070244,\n \"acc_stderr\": 0.013885055359056472\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.000758150113722517,\n \"acc_stderr\": 0.0007581501137225187\n }\n}\n```", "repo_url": "https://huggingface.co/Josephgflowers/TinyLlama-3T-Cinder-v1.2", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2024_01_13T20_09_00.533513", "path": ["**/details_harness|arc:challenge|25_2024-01-13T20-09-00.533513.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-01-13T20-09-00.533513.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_01_13T20_09_00.533513", "path": ["**/details_harness|gsm8k|5_2024-01-13T20-09-00.533513.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-01-13T20-09-00.533513.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_01_13T20_09_00.533513", "path": ["**/details_harness|hellaswag|10_2024-01-13T20-09-00.533513.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-01-13T20-09-00.533513.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_01_13T20_09_00.533513", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T20-09-00.533513.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-01-13T20-09-00.533513.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-01-13T20-09-00.533513.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T20-09-00.533513.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T20-09-00.533513.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-01-13T20-09-00.533513.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T20-09-00.533513.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T20-09-00.533513.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T20-09-00.533513.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T20-09-00.533513.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-01-13T20-09-00.533513.parquet", 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"path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-13T20-09-00.533513.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2024_01_13T20_09_00.533513", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T20-09-00.533513.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T20-09-00.533513.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2024_01_13T20_09_00.533513", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T20-09-00.533513.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T20-09-00.533513.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2024_01_13T20_09_00.533513", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T20-09-00.533513.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T20-09-00.533513.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2024_01_13T20_09_00.533513", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T20-09-00.533513.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T20-09-00.533513.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2024_01_13T20_09_00.533513", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T20-09-00.533513.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T20-09-00.533513.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2024_01_13T20_09_00.533513", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T20-09-00.533513.parquet"]}, {"split": "latest", "path": 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"latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T20-09-00.533513.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_01_13T20_09_00.533513", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T20-09-00.533513.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T20-09-00.533513.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_01_13T20_09_00.533513", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T20-09-00.533513.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T20-09-00.533513.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_01_13T20_09_00.533513", "path": 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["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T20-09-00.533513.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T20-09-00.533513.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_13T20_09_00.533513", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T20-09-00.533513.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T20-09-00.533513.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_13T20_09_00.533513", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T20-09-00.533513.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T20-09-00.533513.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_13T20_09_00.533513", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T20-09-00.533513.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T20-09-00.533513.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T20_09_00.533513", "path": ["**/details_harness|winogrande|5_2024-01-13T20-09-00.533513.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T20-09-00.533513.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T20_09_00.533513", "path": ["results_2024-01-13T20-09-00.533513.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T20-09-00.533513.parquet"]}]}]} | 2024-01-13T20:11:12+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of Josephgflowers/TinyLlama-3T-Cinder-v1.2
Dataset automatically created during the evaluation run of model Josephgflowers/TinyLlama-3T-Cinder-v1.2 on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-13T20:09:00.533513(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of Josephgflowers/TinyLlama-3T-Cinder-v1.2\n\n\n\nDataset automatically created during the evaluation run of model Josephgflowers/TinyLlama-3T-Cinder-v1.2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T20:09:00.533513(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of Josephgflowers/TinyLlama-3T-Cinder-v1.2\n\n\n\nDataset automatically created during the evaluation run of model Josephgflowers/TinyLlama-3T-Cinder-v1.2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T20:09:00.533513(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
c5cfa10a5c05d4a9d90745a3b5e90e7e553c9292 |
# Dataset Card for Evaluation run of NeverSleep/Noromaid-7B-0.4-DPO
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [NeverSleep/Noromaid-7B-0.4-DPO](https://huggingface.co/NeverSleep/Noromaid-7B-0.4-DPO) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_NeverSleep__Noromaid-7B-0.4-DPO",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T20:13:17.595813](https://huggingface.co/datasets/open-llm-leaderboard/details_NeverSleep__Noromaid-7B-0.4-DPO/blob/main/results_2024-01-13T20-13-17.595813.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6276281202486842,
"acc_stderr": 0.032369463494806044,
"acc_norm": 0.6354200747096772,
"acc_norm_stderr": 0.033039898413677445,
"mc1": 0.2778457772337821,
"mc1_stderr": 0.015680929364024647,
"mc2": 0.4227934173655964,
"mc2_stderr": 0.014275177541071271
},
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}
}
```
## Dataset Details
### Dataset Description
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## Uses
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### Direct Use
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## Dataset Structure
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_NeverSleep__Noromaid-7B-0.4-DPO | [
"region:us"
] | 2024-01-13T20:12:28+00:00 | {"pretty_name": "Evaluation run of NeverSleep/Noromaid-7B-0.4-DPO", "dataset_summary": "Dataset automatically created during the evaluation run of model [NeverSleep/Noromaid-7B-0.4-DPO](https://huggingface.co/NeverSleep/Noromaid-7B-0.4-DPO) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_NeverSleep__Noromaid-7B-0.4-DPO\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T20:13:17.595813](https://huggingface.co/datasets/open-llm-leaderboard/details_NeverSleep__Noromaid-7B-0.4-DPO/blob/main/results_2024-01-13T20-13-17.595813.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6276281202486842,\n \"acc_stderr\": 0.032369463494806044,\n \"acc_norm\": 0.6354200747096772,\n \"acc_norm_stderr\": 0.033039898413677445,\n \"mc1\": 0.2778457772337821,\n \"mc1_stderr\": 0.015680929364024647,\n \"mc2\": 0.4227934173655964,\n \"mc2_stderr\": 0.014275177541071271\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.591296928327645,\n \"acc_stderr\": 0.014365750345427006,\n \"acc_norm\": 0.6228668941979523,\n \"acc_norm_stderr\": 0.014163366896192603\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6459868552081258,\n \"acc_stderr\": 0.004772358395130453,\n \"acc_norm\": 0.8431587333200558,\n \"acc_norm_stderr\": 0.0036290784658809666\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 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{"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T20-13-17.595813.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2024_01_13T20_10_12.408839", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T20-10-12.408839.parquet"]}, {"split": "2024_01_13T20_13_17.595813", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T20-13-17.595813.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T20-13-17.595813.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2024_01_13T20_10_12.408839", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T20-10-12.408839.parquet"]}, {"split": "2024_01_13T20_13_17.595813", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T20-13-17.595813.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T20-13-17.595813.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_01_13T20_10_12.408839", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T20-10-12.408839.parquet"]}, {"split": "2024_01_13T20_13_17.595813", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T20-13-17.595813.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T20-13-17.595813.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_01_13T20_10_12.408839", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T20-10-12.408839.parquet"]}, {"split": "2024_01_13T20_13_17.595813", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T20-13-17.595813.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T20-13-17.595813.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_01_13T20_10_12.408839", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T20-10-12.408839.parquet"]}, {"split": "2024_01_13T20_13_17.595813", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T20-13-17.595813.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T20-13-17.595813.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_01_13T20_10_12.408839", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T20-10-12.408839.parquet"]}, {"split": "2024_01_13T20_13_17.595813", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T20-13-17.595813.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T20-13-17.595813.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_01_13T20_10_12.408839", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T20-10-12.408839.parquet"]}, {"split": "2024_01_13T20_13_17.595813", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T20-13-17.595813.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T20-13-17.595813.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_01_13T20_10_12.408839", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T20-10-12.408839.parquet"]}, {"split": "2024_01_13T20_13_17.595813", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T20-13-17.595813.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T20-13-17.595813.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_13T20_10_12.408839", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T20-10-12.408839.parquet"]}, {"split": "2024_01_13T20_13_17.595813", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T20-13-17.595813.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T20-13-17.595813.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_13T20_10_12.408839", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T20-10-12.408839.parquet"]}, {"split": "2024_01_13T20_13_17.595813", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T20-13-17.595813.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T20-13-17.595813.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_13T20_10_12.408839", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T20-10-12.408839.parquet"]}, {"split": "2024_01_13T20_13_17.595813", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T20-13-17.595813.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T20-13-17.595813.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T20_10_12.408839", "path": ["**/details_harness|winogrande|5_2024-01-13T20-10-12.408839.parquet"]}, {"split": "2024_01_13T20_13_17.595813", "path": ["**/details_harness|winogrande|5_2024-01-13T20-13-17.595813.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T20-13-17.595813.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T20_10_12.408839", "path": ["results_2024-01-13T20-10-12.408839.parquet"]}, {"split": "2024_01_13T20_13_17.595813", "path": ["results_2024-01-13T20-13-17.595813.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T20-13-17.595813.parquet"]}]}]} | 2024-01-13T20:15:57+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of NeverSleep/Noromaid-7B-0.4-DPO
Dataset automatically created during the evaluation run of model NeverSleep/Noromaid-7B-0.4-DPO on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-13T20:13:17.595813(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of NeverSleep/Noromaid-7B-0.4-DPO\n\n\n\nDataset automatically created during the evaluation run of model NeverSleep/Noromaid-7B-0.4-DPO on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T20:13:17.595813(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of NeverSleep/Noromaid-7B-0.4-DPO\n\n\n\nDataset automatically created during the evaluation run of model NeverSleep/Noromaid-7B-0.4-DPO on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T20:13:17.595813(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
8856d77be1fa398343944c577121e474307744bd |
# Dataset Card for Evaluation run of Heng666/EastAsia-4x7B-Moe-experiment
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Heng666/EastAsia-4x7B-Moe-experiment](https://huggingface.co/Heng666/EastAsia-4x7B-Moe-experiment) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Heng666__EastAsia-4x7B-Moe-experiment",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T20:13:26.572648](https://huggingface.co/datasets/open-llm-leaderboard/details_Heng666__EastAsia-4x7B-Moe-experiment/blob/main/results_2024-01-13T20-13-26.572648.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.5469974383782618,
"acc_stderr": 0.03393213465072408,
"acc_norm": 0.5579608379948889,
"acc_norm_stderr": 0.03483564278598156,
"mc1": 0.2937576499388005,
"mc1_stderr": 0.015945068581236618,
"mc2": 0.4982979181810864,
"mc2_stderr": 0.016572977538918135
},
"harness|arc:challenge|25": {
"acc": 0.36006825938566556,
"acc_stderr": 0.014027516814585188,
"acc_norm": 0.39505119453924914,
"acc_norm_stderr": 0.014285898292938163
},
"harness|hellaswag|10": {
"acc": 0.3889663413662617,
"acc_stderr": 0.004865193237024052,
"acc_norm": 0.4892451702848038,
"acc_norm_stderr": 0.0049886269781730976
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5555555555555556,
"acc_stderr": 0.04292596718256981,
"acc_norm": 0.5555555555555556,
"acc_norm_stderr": 0.04292596718256981
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.625,
"acc_stderr": 0.039397364351956274,
"acc_norm": 0.625,
"acc_norm_stderr": 0.039397364351956274
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.55,
"acc_stderr": 0.04999999999999999,
"acc_norm": 0.55,
"acc_norm_stderr": 0.04999999999999999
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6113207547169811,
"acc_stderr": 0.030000485448675986,
"acc_norm": 0.6113207547169811,
"acc_norm_stderr": 0.030000485448675986
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6527777777777778,
"acc_stderr": 0.039812405437178615,
"acc_norm": 0.6527777777777778,
"acc_norm_stderr": 0.039812405437178615
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.38,
"acc_stderr": 0.048783173121456316,
"acc_norm": 0.38,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.38,
"acc_stderr": 0.04878317312145632,
"acc_norm": 0.38,
"acc_norm_stderr": 0.04878317312145632
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252605,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252605
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5780346820809249,
"acc_stderr": 0.03765746693865149,
"acc_norm": 0.5780346820809249,
"acc_norm_stderr": 0.03765746693865149
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.46078431372549017,
"acc_stderr": 0.04959859966384181,
"acc_norm": 0.46078431372549017,
"acc_norm_stderr": 0.04959859966384181
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.68,
"acc_stderr": 0.04688261722621505,
"acc_norm": 0.68,
"acc_norm_stderr": 0.04688261722621505
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.4425531914893617,
"acc_stderr": 0.032469569197899575,
"acc_norm": 0.4425531914893617,
"acc_norm_stderr": 0.032469569197899575
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.40350877192982454,
"acc_stderr": 0.046151869625837026,
"acc_norm": 0.40350877192982454,
"acc_norm_stderr": 0.046151869625837026
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5103448275862069,
"acc_stderr": 0.04165774775728763,
"acc_norm": 0.5103448275862069,
"acc_norm_stderr": 0.04165774775728763
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.3333333333333333,
"acc_stderr": 0.024278568024307712,
"acc_norm": 0.3333333333333333,
"acc_norm_stderr": 0.024278568024307712
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.3333333333333333,
"acc_stderr": 0.04216370213557835,
"acc_norm": 0.3333333333333333,
"acc_norm_stderr": 0.04216370213557835
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.39,
"acc_stderr": 0.04902071300001974,
"acc_norm": 0.39,
"acc_norm_stderr": 0.04902071300001974
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.667741935483871,
"acc_stderr": 0.0267955608481228,
"acc_norm": 0.667741935483871,
"acc_norm_stderr": 0.0267955608481228
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.4433497536945813,
"acc_stderr": 0.03495334582162933,
"acc_norm": 0.4433497536945813,
"acc_norm_stderr": 0.03495334582162933
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.63,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.63,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.6363636363636364,
"acc_stderr": 0.03756335775187898,
"acc_norm": 0.6363636363636364,
"acc_norm_stderr": 0.03756335775187898
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.696969696969697,
"acc_stderr": 0.032742879140268674,
"acc_norm": 0.696969696969697,
"acc_norm_stderr": 0.032742879140268674
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8031088082901554,
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"acc_norm": 0.8031088082901554,
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"harness|hendrycksTest-high_school_world_history|5": {
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"harness|hendrycksTest-us_foreign_policy|5": {
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"harness|hendrycksTest-virology|5": {
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"harness|hendrycksTest-world_religions|5": {
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"harness|winogrande|5": {
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"harness|gsm8k|5": {
"acc": 0.001516300227445034,
"acc_stderr": 0.001071779348549261
}
}
```
## Dataset Details
### Dataset Description
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## Uses
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## Dataset Structure
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## Dataset Creation
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### Source Data
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#### Data Collection and Processing
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#### Personal and Sensitive Information
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_Heng666__EastAsia-4x7B-Moe-experiment | [
"region:us"
] | 2024-01-13T20:15:42+00:00 | {"pretty_name": "Evaluation run of Heng666/EastAsia-4x7B-Moe-experiment", "dataset_summary": "Dataset automatically created during the evaluation run of model [Heng666/EastAsia-4x7B-Moe-experiment](https://huggingface.co/Heng666/EastAsia-4x7B-Moe-experiment) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Heng666__EastAsia-4x7B-Moe-experiment\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T20:13:26.572648](https://huggingface.co/datasets/open-llm-leaderboard/details_Heng666__EastAsia-4x7B-Moe-experiment/blob/main/results_2024-01-13T20-13-26.572648.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.5469974383782618,\n \"acc_stderr\": 0.03393213465072408,\n \"acc_norm\": 0.5579608379948889,\n \"acc_norm_stderr\": 0.03483564278598156,\n \"mc1\": 0.2937576499388005,\n \"mc1_stderr\": 0.015945068581236618,\n \"mc2\": 0.4982979181810864,\n \"mc2_stderr\": 0.016572977538918135\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.36006825938566556,\n \"acc_stderr\": 0.014027516814585188,\n \"acc_norm\": 0.39505119453924914,\n \"acc_norm_stderr\": 0.014285898292938163\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.3889663413662617,\n \"acc_stderr\": 0.004865193237024052,\n \"acc_norm\": 0.4892451702848038,\n \"acc_norm_stderr\": 0.0049886269781730976\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5555555555555556,\n \"acc_stderr\": 0.04292596718256981,\n \"acc_norm\": 0.5555555555555556,\n \"acc_norm_stderr\": 0.04292596718256981\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.625,\n \"acc_stderr\": 0.039397364351956274,\n \"acc_norm\": 0.625,\n \"acc_norm_stderr\": 0.039397364351956274\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.55,\n \"acc_stderr\": 0.04999999999999999,\n \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.04999999999999999\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6113207547169811,\n \"acc_stderr\": 0.030000485448675986,\n \"acc_norm\": 0.6113207547169811,\n \"acc_norm_stderr\": 0.030000485448675986\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6527777777777778,\n \"acc_stderr\": 0.039812405437178615,\n \"acc_norm\": 0.6527777777777778,\n \"acc_norm_stderr\": 0.039812405437178615\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5780346820809249,\n \"acc_stderr\": 0.03765746693865149,\n \"acc_norm\": 0.5780346820809249,\n \"acc_norm_stderr\": 0.03765746693865149\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.46078431372549017,\n \"acc_stderr\": 0.04959859966384181,\n \"acc_norm\": 0.46078431372549017,\n \"acc_norm_stderr\": 0.04959859966384181\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.4425531914893617,\n \"acc_stderr\": 0.032469569197899575,\n \"acc_norm\": 0.4425531914893617,\n \"acc_norm_stderr\": 0.032469569197899575\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.40350877192982454,\n \"acc_stderr\": 0.046151869625837026,\n \"acc_norm\": 0.40350877192982454,\n \"acc_norm_stderr\": 0.046151869625837026\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5103448275862069,\n \"acc_stderr\": 0.04165774775728763,\n \"acc_norm\": 0.5103448275862069,\n \"acc_norm_stderr\": 0.04165774775728763\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.024278568024307712,\n \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.024278568024307712\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.04216370213557835,\n \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.04216370213557835\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001974,\n \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001974\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.667741935483871,\n \"acc_stderr\": 0.0267955608481228,\n \"acc_norm\": 0.667741935483871,\n \"acc_norm_stderr\": 0.0267955608481228\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.4433497536945813,\n \"acc_stderr\": 0.03495334582162933,\n \"acc_norm\": 0.4433497536945813,\n \"acc_norm_stderr\": 0.03495334582162933\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.63,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.6363636363636364,\n \"acc_stderr\": 0.03756335775187898,\n \"acc_norm\": 0.6363636363636364,\n \"acc_norm_stderr\": 0.03756335775187898\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.696969696969697,\n \"acc_stderr\": 0.032742879140268674,\n \"acc_norm\": 0.696969696969697,\n \"acc_norm_stderr\": 0.032742879140268674\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8031088082901554,\n \"acc_stderr\": 0.02869787397186067,\n \"acc_norm\": 0.8031088082901554,\n \"acc_norm_stderr\": 0.02869787397186067\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.5743589743589743,\n \"acc_stderr\": 0.025069094387296532,\n \"acc_norm\": 0.5743589743589743,\n \"acc_norm_stderr\": 0.025069094387296532\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.3148148148148148,\n \"acc_stderr\": 0.028317533496066485,\n \"acc_norm\": 0.3148148148148148,\n \"acc_norm_stderr\": 0.028317533496066485\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": 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"latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T20-13-26.572648.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_01_13T20_13_26.572648", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T20-13-26.572648.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T20-13-26.572648.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_01_13T20_13_26.572648", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T20-13-26.572648.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T20-13-26.572648.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_01_13T20_13_26.572648", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T20-13-26.572648.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T20-13-26.572648.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_01_13T20_13_26.572648", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T20-13-26.572648.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T20-13-26.572648.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_01_13T20_13_26.572648", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T20-13-26.572648.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T20-13-26.572648.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_01_13T20_13_26.572648", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T20-13-26.572648.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T20-13-26.572648.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_13T20_13_26.572648", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T20-13-26.572648.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T20-13-26.572648.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_13T20_13_26.572648", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T20-13-26.572648.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T20-13-26.572648.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_13T20_13_26.572648", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T20-13-26.572648.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T20-13-26.572648.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T20_13_26.572648", "path": ["**/details_harness|winogrande|5_2024-01-13T20-13-26.572648.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T20-13-26.572648.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T20_13_26.572648", "path": ["results_2024-01-13T20-13-26.572648.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T20-13-26.572648.parquet"]}]}]} | 2024-01-13T20:16:08+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of Heng666/EastAsia-4x7B-Moe-experiment
Dataset automatically created during the evaluation run of model Heng666/EastAsia-4x7B-Moe-experiment on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-13T20:13:26.572648(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of Heng666/EastAsia-4x7B-Moe-experiment\n\n\n\nDataset automatically created during the evaluation run of model Heng666/EastAsia-4x7B-Moe-experiment on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T20:13:26.572648(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of Heng666/EastAsia-4x7B-Moe-experiment\n\n\n\nDataset automatically created during the evaluation run of model Heng666/EastAsia-4x7B-Moe-experiment on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T20:13:26.572648(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
b700d93c3cf75e58986891b3f95f3712b69417df |
# Dataset of matsukaze/松風/松风 (Azur Lane)
This is the dataset of matsukaze/松風/松风 (Azur Lane), containing 21 images and their tags.
The core tags of this character are `animal_ears, yellow_eyes, black_hair, fox_ears, long_hair, brown_hair, ponytail, tail, multicolored_hair, hair_between_eyes, hair_ornament, bangs, brown_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:--------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 21 | 20.69 MiB | [Download](https://huggingface.co/datasets/CyberHarem/matsukaze_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 21 | 14.33 MiB | [Download](https://huggingface.co/datasets/CyberHarem/matsukaze_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 47 | 28.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/matsukaze_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 21 | 18.50 MiB | [Download](https://huggingface.co/datasets/CyberHarem/matsukaze_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 47 | 35.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/matsukaze_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/matsukaze_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------|
| 0 | 21 |  |  |  |  |  | 1girl, open_mouth, looking_at_viewer, solo, hakama_skirt, wide_sleeves, long_sleeves, simple_background, smile |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | open_mouth | looking_at_viewer | solo | hakama_skirt | wide_sleeves | long_sleeves | simple_background | smile |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------------|:--------------------|:-------|:---------------|:---------------|:---------------|:--------------------|:--------|
| 0 | 21 |  |  |  |  |  | X | X | X | X | X | X | X | X | X |
| CyberHarem/matsukaze_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T20:21:11+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T20:32:52+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of matsukaze/松風/松风 (Azur Lane)
======================================
This is the dataset of matsukaze/松風/松风 (Azur Lane), containing 21 images and their tags.
The core tags of this character are 'animal\_ears, yellow\_eyes, black\_hair, fox\_ears, long\_hair, brown\_hair, ponytail, tail, multicolored\_hair, hair\_between\_eyes, hair\_ornament, bangs, brown\_eyes', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
008b95c605a3c542bc740e155e6b3f9263be16be |
# Dataset of leander/リアンダー/利安得 (Azur Lane)
This is the dataset of leander/リアンダー/利安得 (Azur Lane), containing 91 images and their tags.
The core tags of this character are `blonde_hair, blue_eyes, long_hair, breasts, hairband, large_breasts, bangs, hair_between_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 91 | 124.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/leander_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 91 | 69.90 MiB | [Download](https://huggingface.co/datasets/CyberHarem/leander_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 223 | 151.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/leander_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 91 | 110.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/leander_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 223 | 218.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/leander_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/leander_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 19 |  |  |  |  |  | 1girl, looking_at_viewer, solo, retrofit_(azur_lane), red_skirt, white_gloves, fingerless_gloves, smile, garter_straps, black_thighhighs, blush, pleated_skirt, short_sleeves, white_shirt, medium_breasts, open_mouth |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | retrofit_(azur_lane) | red_skirt | white_gloves | fingerless_gloves | smile | garter_straps | black_thighhighs | blush | pleated_skirt | short_sleeves | white_shirt | medium_breasts | open_mouth |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:-----------------------|:------------|:---------------|:--------------------|:--------|:----------------|:-------------------|:--------|:----------------|:----------------|:--------------|:-----------------|:-------------|
| 0 | 19 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/leander_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T20:21:19+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T20:48:27+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of leander/リアンダー/利安得 (Azur Lane)
========================================
This is the dataset of leander/リアンダー/利安得 (Azur Lane), containing 91 images and their tags.
The core tags of this character are 'blonde\_hair, blue\_eyes, long\_hair, breasts, hairband, large\_breasts, bangs, hair\_between\_eyes', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
2d05176676d3b42019c775b39cf37a81d8d360b9 |
# Dataset of manchester/マンチェスター/曼彻斯特 (Azur Lane)
This is the dataset of manchester/マンチェスター/曼彻斯特 (Azur Lane), containing 29 images and their tags.
The core tags of this character are `breasts, bangs, large_breasts, grey_hair, green_eyes, hair_bun, maid_headdress, short_hair, hat, nurse_cap, symbol-shaped_pupils`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:---------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 29 | 58.69 MiB | [Download](https://huggingface.co/datasets/CyberHarem/manchester_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 29 | 25.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/manchester_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 74 | 60.68 MiB | [Download](https://huggingface.co/datasets/CyberHarem/manchester_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 29 | 47.11 MiB | [Download](https://huggingface.co/datasets/CyberHarem/manchester_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 74 | 98.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/manchester_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/manchester_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 14 |  |  |  |  |  | 1girl, looking_at_viewer, navel, solo, black_bikini, blush, smile, collarbone, cleavage, open_mouth, twintails, aqua_eyes, maid_bikini, bare_shoulders, black_choker, frilled_bikini, outdoors, sitting, twin_braids, wrist_cuffs, bridal_garter, closed_mouth, nipples, side-tie_bikini_bottom, x_hair_ornament |
| 1 | 10 |  |  |  |  |  | 1girl, looking_at_viewer, solo, white_gloves, blush, nurse, short_sleeves, shrug_(clothing), white_thighhighs, bra, demon_wings, holding_syringe, demon_tail, heart-shaped_pupils, navel, open_mouth, simple_background, sitting, smile, white_skirt, garter_straps, medium_breasts, single_hair_bun, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | navel | solo | black_bikini | blush | smile | collarbone | cleavage | open_mouth | twintails | aqua_eyes | maid_bikini | bare_shoulders | black_choker | frilled_bikini | outdoors | sitting | twin_braids | wrist_cuffs | bridal_garter | closed_mouth | nipples | side-tie_bikini_bottom | x_hair_ornament | white_gloves | nurse | short_sleeves | shrug_(clothing) | white_thighhighs | bra | demon_wings | holding_syringe | demon_tail | heart-shaped_pupils | simple_background | white_skirt | garter_straps | medium_breasts | single_hair_bun | white_background |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:--------|:-------|:---------------|:--------|:--------|:-------------|:-----------|:-------------|:------------|:------------|:--------------|:-----------------|:---------------|:-----------------|:-----------|:----------|:--------------|:--------------|:----------------|:---------------|:----------|:-------------------------|:------------------|:---------------|:--------|:----------------|:-------------------|:-------------------|:------|:--------------|:------------------|:-------------|:----------------------|:--------------------|:--------------|:----------------|:-----------------|:------------------|:-------------------|
| 0 | 14 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | |
| 1 | 10 |  |  |  |  |  | X | X | X | X | | X | X | | | X | | | | | | | | X | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/manchester_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T20:21:19+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T20:34:48+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of manchester/マンチェスター/曼彻斯特 (Azur Lane)
==============================================
This is the dataset of manchester/マンチェスター/曼彻斯特 (Azur Lane), containing 29 images and their tags.
The core tags of this character are 'breasts, bangs, large\_breasts, grey\_hair, green\_eyes, hair\_bun, maid\_headdress, short\_hair, hat, nurse\_cap, symbol-shaped\_pupils', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
dd81daadb9889fe05581e57dc9b743d8524c0640 |
# Dataset of trento/トレント/特伦托 (Azur Lane)
This is the dataset of trento/トレント/特伦托 (Azur Lane), containing 60 images and their tags.
The core tags of this character are `long_hair, breasts, hair_over_one_eye, large_breasts, purple_hair, red_eyes, bangs, very_long_hair, eyewear_on_head, sunglasses, blue_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 60 | 87.22 MiB | [Download](https://huggingface.co/datasets/CyberHarem/trento_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 60 | 47.98 MiB | [Download](https://huggingface.co/datasets/CyberHarem/trento_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 145 | 106.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/trento_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 60 | 76.32 MiB | [Download](https://huggingface.co/datasets/CyberHarem/trento_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 145 | 151.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/trento_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/trento_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 14 |  |  |  |  |  | 1girl, black_bikini, cleavage, navel, solo, blush, looking_at_viewer, o-ring_bikini, bare_shoulders, smile, thigh_strap, collarbone, wrist_scrunchie, black_choker, thighs, bead_bracelet, open_mouth, simple_background, stomach, official_alternate_costume, side-tie_bikini_bottom, closed_mouth, multi-strapped_bikini, o-ring_top, mole, thigh_gap, wet, white_background |
| 1 | 5 |  |  |  |  |  | black_bikini, blue_sky, day, looking_at_viewer, navel, official_alternate_costume, open_mouth, 1girl, cleavage, cowboy_shot, multi-strapped_bikini, o-ring_bikini, outdoors, solo, :d, cloud, collarbone, side-tie_bikini_bottom, black_choker, bracelet, halterneck, ocean, skindentation, standing, thigh_strap |
| 2 | 12 |  |  |  |  |  | looking_at_viewer, 1girl, solo, white_gloves, cape, garter_straps, simple_background, smile, epaulettes, white_background, blush, dress, standing, black_thighhighs, boots, cleavage, red_necktie |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_bikini | cleavage | navel | solo | blush | looking_at_viewer | o-ring_bikini | bare_shoulders | smile | thigh_strap | collarbone | wrist_scrunchie | black_choker | thighs | bead_bracelet | open_mouth | simple_background | stomach | official_alternate_costume | side-tie_bikini_bottom | closed_mouth | multi-strapped_bikini | o-ring_top | mole | thigh_gap | wet | white_background | blue_sky | day | cowboy_shot | outdoors | :d | cloud | bracelet | halterneck | ocean | skindentation | standing | white_gloves | cape | garter_straps | epaulettes | dress | black_thighhighs | boots | red_necktie |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:-----------|:--------|:-------|:--------|:--------------------|:----------------|:-----------------|:--------|:--------------|:-------------|:------------------|:---------------|:---------|:----------------|:-------------|:--------------------|:----------|:-----------------------------|:-------------------------|:---------------|:------------------------|:-------------|:-------|:------------|:------|:-------------------|:-----------|:------|:--------------|:-----------|:-----|:--------|:-----------|:-------------|:--------|:----------------|:-----------|:---------------|:-------|:----------------|:-------------|:--------|:-------------------|:--------|:--------------|
| 0 | 14 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | X | X | X | | X | X | | | X | X | | X | | | X | | | X | X | | X | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | |
| 2 | 12 |  |  |  |  |  | X | | X | | X | X | X | | | X | | | | | | | | X | | | | | | | | | | X | | | | | | | | | | | X | X | X | X | X | X | X | X | X |
| CyberHarem/trento_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T20:21:21+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T20:39:44+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of trento/トレント/特伦托 (Azur Lane)
======================================
This is the dataset of trento/トレント/特伦托 (Azur Lane), containing 60 images and their tags.
The core tags of this character are 'long\_hair, breasts, hair\_over\_one\_eye, large\_breasts, purple\_hair, red\_eyes, bangs, very\_long\_hair, eyewear\_on\_head, sunglasses, blue\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
7d91a2257dcfbbbed21afc218fc58a732e4cd204 |
# Dataset of kent/ケント/肯特 (Azur Lane)
This is the dataset of kent/ケント/肯特 (Azur Lane), containing 24 images and their tags.
The core tags of this character are `breasts, red_eyes, hairband, hair_between_eyes, large_breasts, short_hair, fang, purple_hair, bangs, ahoge, bow, ribbon, black_hairband`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:---------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 24 | 29.93 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kent_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 24 | 16.20 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kent_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 61 | 38.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kent_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 24 | 25.78 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kent_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 61 | 56.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kent_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/kent_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 24 |  |  |  |  |  | 1girl, solo, looking_at_viewer, open_mouth, blush, bare_shoulders, black_gloves, elbow_gloves, simple_background, sleeveless_shirt, upper_body, white_background, white_shirt, :d, skirt, white_apron |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | open_mouth | blush | bare_shoulders | black_gloves | elbow_gloves | simple_background | sleeveless_shirt | upper_body | white_background | white_shirt | :d | skirt | white_apron |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:-------------|:--------|:-----------------|:---------------|:---------------|:--------------------|:-------------------|:-------------|:-------------------|:--------------|:-----|:--------|:--------------|
| 0 | 24 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/kent_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T20:21:37+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T20:32:39+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of kent/ケント/肯特 (Azur Lane)
==================================
This is the dataset of kent/ケント/肯特 (Azur Lane), containing 24 images and their tags.
The core tags of this character are 'breasts, red\_eyes, hairband, hair\_between\_eyes, large\_breasts, short\_hair, fang, purple\_hair, bangs, ahoge, bow, ribbon, black\_hairband', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
e6569ed2960624988cfb36d9025040f7fc0be7c1 |
# Dataset Card for Evaluation run of charlesdedampierre/TopicNeuralHermes-2.5-Mistral-7B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [charlesdedampierre/TopicNeuralHermes-2.5-Mistral-7B](https://huggingface.co/charlesdedampierre/TopicNeuralHermes-2.5-Mistral-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_charlesdedampierre__TopicNeuralHermes-2.5-Mistral-7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T20:19:31.036723](https://huggingface.co/datasets/open-llm-leaderboard/details_charlesdedampierre__TopicNeuralHermes-2.5-Mistral-7B/blob/main/results_2024-01-13T20-19-31.036723.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6377095946098038,
"acc_stderr": 0.03228135828297783,
"acc_norm": 0.64089103621422,
"acc_norm_stderr": 0.032919379883826004,
"mc1": 0.37454100367197063,
"mc1_stderr": 0.016943535128405338,
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}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## Dataset Structure
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Who are the source data producers?
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#### Annotation process
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#### Personal and Sensitive Information
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## Bias, Risks, and Limitations
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_charlesdedampierre__TopicNeuralHermes-2.5-Mistral-7B | [
"region:us"
] | 2024-01-13T20:21:50+00:00 | {"pretty_name": "Evaluation run of charlesdedampierre/TopicNeuralHermes-2.5-Mistral-7B", "dataset_summary": "Dataset automatically created during the evaluation run of model [charlesdedampierre/TopicNeuralHermes-2.5-Mistral-7B](https://huggingface.co/charlesdedampierre/TopicNeuralHermes-2.5-Mistral-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_charlesdedampierre__TopicNeuralHermes-2.5-Mistral-7B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T20:19:31.036723](https://huggingface.co/datasets/open-llm-leaderboard/details_charlesdedampierre__TopicNeuralHermes-2.5-Mistral-7B/blob/main/results_2024-01-13T20-19-31.036723.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6377095946098038,\n \"acc_stderr\": 0.03228135828297783,\n \"acc_norm\": 0.64089103621422,\n \"acc_norm_stderr\": 0.032919379883826004,\n \"mc1\": 0.37454100367197063,\n \"mc1_stderr\": 0.016943535128405338,\n \"mc2\": 0.5546784964324225,\n \"mc2_stderr\": 0.015236087364473834\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6254266211604096,\n \"acc_stderr\": 0.014144193471893456,\n \"acc_norm\": 0.6706484641638225,\n \"acc_norm_stderr\": 0.013734057652635474\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6623182632941645,\n \"acc_stderr\": 0.004719529099913136,\n \"acc_norm\": 0.8544114718183629,\n \"acc_norm_stderr\": 0.003519724163310887\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 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["**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T20-19-31.036723.parquet"]}]}, {"config_name": "harness_hendrycksTest_international_law_5", "data_files": [{"split": "2024_01_13T20_19_31.036723", "path": ["**/details_harness|hendrycksTest-international_law|5_2024-01-13T20-19-31.036723.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-international_law|5_2024-01-13T20-19-31.036723.parquet"]}]}, {"config_name": "harness_hendrycksTest_jurisprudence_5", "data_files": [{"split": "2024_01_13T20_19_31.036723", "path": ["**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T20-19-31.036723.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T20-19-31.036723.parquet"]}]}, {"config_name": "harness_hendrycksTest_logical_fallacies_5", "data_files": [{"split": "2024_01_13T20_19_31.036723", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T20-19-31.036723.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T20-19-31.036723.parquet"]}]}, {"config_name": "harness_hendrycksTest_machine_learning_5", "data_files": [{"split": "2024_01_13T20_19_31.036723", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T20-19-31.036723.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T20-19-31.036723.parquet"]}]}, {"config_name": "harness_hendrycksTest_management_5", "data_files": [{"split": "2024_01_13T20_19_31.036723", "path": ["**/details_harness|hendrycksTest-management|5_2024-01-13T20-19-31.036723.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-management|5_2024-01-13T20-19-31.036723.parquet"]}]}, {"config_name": "harness_hendrycksTest_marketing_5", "data_files": [{"split": "2024_01_13T20_19_31.036723", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-13T20-19-31.036723.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-13T20-19-31.036723.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2024_01_13T20_19_31.036723", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T20-19-31.036723.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T20-19-31.036723.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2024_01_13T20_19_31.036723", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T20-19-31.036723.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T20-19-31.036723.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2024_01_13T20_19_31.036723", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T20-19-31.036723.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T20-19-31.036723.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2024_01_13T20_19_31.036723", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T20-19-31.036723.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T20-19-31.036723.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2024_01_13T20_19_31.036723", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T20-19-31.036723.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T20-19-31.036723.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2024_01_13T20_19_31.036723", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T20-19-31.036723.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T20-19-31.036723.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2024_01_13T20_19_31.036723", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T20-19-31.036723.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T20-19-31.036723.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2024_01_13T20_19_31.036723", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T20-19-31.036723.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T20-19-31.036723.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2024_01_13T20_19_31.036723", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T20-19-31.036723.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T20-19-31.036723.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_01_13T20_19_31.036723", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T20-19-31.036723.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T20-19-31.036723.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_01_13T20_19_31.036723", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T20-19-31.036723.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T20-19-31.036723.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_01_13T20_19_31.036723", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T20-19-31.036723.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T20-19-31.036723.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_01_13T20_19_31.036723", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T20-19-31.036723.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T20-19-31.036723.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_01_13T20_19_31.036723", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T20-19-31.036723.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T20-19-31.036723.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_01_13T20_19_31.036723", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T20-19-31.036723.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T20-19-31.036723.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_13T20_19_31.036723", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T20-19-31.036723.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T20-19-31.036723.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_13T20_19_31.036723", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T20-19-31.036723.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T20-19-31.036723.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_13T20_19_31.036723", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T20-19-31.036723.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T20-19-31.036723.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T20_19_31.036723", "path": ["**/details_harness|winogrande|5_2024-01-13T20-19-31.036723.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T20-19-31.036723.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T20_19_31.036723", "path": ["results_2024-01-13T20-19-31.036723.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T20-19-31.036723.parquet"]}]}]} | 2024-01-13T20:22:11+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of charlesdedampierre/TopicNeuralHermes-2.5-Mistral-7B
Dataset automatically created during the evaluation run of model charlesdedampierre/TopicNeuralHermes-2.5-Mistral-7B on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-13T20:19:31.036723(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of charlesdedampierre/TopicNeuralHermes-2.5-Mistral-7B\n\n\n\nDataset automatically created during the evaluation run of model charlesdedampierre/TopicNeuralHermes-2.5-Mistral-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T20:19:31.036723(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of charlesdedampierre/TopicNeuralHermes-2.5-Mistral-7B\n\n\n\nDataset automatically created during the evaluation run of model charlesdedampierre/TopicNeuralHermes-2.5-Mistral-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T20:19:31.036723(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
5744b5a3ae05159b541531e6b2f949a92490b8bf |
# Dataset of asanagi/朝凪/朝凪 (Azur Lane)
This is the dataset of asanagi/朝凪/朝凪 (Azur Lane), containing 34 images and their tags.
The core tags of this character are `animal_ears, long_hair, bangs, animal_ear_fluff, yellow_eyes, fox_ears, twintails, blunt_bangs, breasts, grey_hair, fox_girl, tail, braid, very_long_hair, fox_tail, small_breasts, bow, white_hair, hair_bow, red_bow, fang`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 34 | 58.70 MiB | [Download](https://huggingface.co/datasets/CyberHarem/asanagi_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 34 | 25.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/asanagi_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 88 | 60.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/asanagi_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 34 | 47.47 MiB | [Download](https://huggingface.co/datasets/CyberHarem/asanagi_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 88 | 94.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/asanagi_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/asanagi_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 10 |  |  |  |  |  | 1girl, looking_at_viewer, white_thighhighs, solo, white_bikini, open_mouth, blush, micro_bikini, navel, smile, collarbone, side-tie_bikini_bottom, armpits, day, skindentation, sky, toeless_legwear |
| 1 | 15 |  |  |  |  |  | 1girl, solo, looking_at_viewer, detached_sleeves, black_thighhighs, open_mouth, black_gloves, blush, fingerless_gloves, simple_background, leotard, wide_sleeves, :d, japanese_clothes, navel, sword, white_background, bare_shoulders, clothing_cutout, fangs, ribbon_trim, skirt |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | white_thighhighs | solo | white_bikini | open_mouth | blush | micro_bikini | navel | smile | collarbone | side-tie_bikini_bottom | armpits | day | skindentation | sky | toeless_legwear | detached_sleeves | black_thighhighs | black_gloves | fingerless_gloves | simple_background | leotard | wide_sleeves | :d | japanese_clothes | sword | white_background | bare_shoulders | clothing_cutout | fangs | ribbon_trim | skirt |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------------------|:-------|:---------------|:-------------|:--------|:---------------|:--------|:--------|:-------------|:-------------------------|:----------|:------|:----------------|:------|:------------------|:-------------------|:-------------------|:---------------|:--------------------|:--------------------|:----------|:---------------|:-----|:-------------------|:--------|:-------------------|:-----------------|:------------------|:--------|:--------------|:--------|
| 0 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | |
| 1 | 15 |  |  |  |  |  | X | X | | X | | X | X | | X | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/asanagi_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T20:21:56+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T20:29:33+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of asanagi/朝凪/朝凪 (Azur Lane)
====================================
This is the dataset of asanagi/朝凪/朝凪 (Azur Lane), containing 34 images and their tags.
The core tags of this character are 'animal\_ears, long\_hair, bangs, animal\_ear\_fluff, yellow\_eyes, fox\_ears, twintails, blunt\_bangs, breasts, grey\_hair, fox\_girl, tail, braid, very\_long\_hair, fox\_tail, small\_breasts, bow, white\_hair, hair\_bow, red\_bow, fang', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
9b230530b84c0188ecca6e31c971c828de4e00da |
# Dataset Card for Evaluation run of diffnamehard/Psyfighter2-Noromaid-ties-Capybara-13B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [diffnamehard/Psyfighter2-Noromaid-ties-Capybara-13B](https://huggingface.co/diffnamehard/Psyfighter2-Noromaid-ties-Capybara-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_diffnamehard__Psyfighter2-Noromaid-ties-Capybara-13B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T20:20:55.847857](https://huggingface.co/datasets/open-llm-leaderboard/details_diffnamehard__Psyfighter2-Noromaid-ties-Capybara-13B/blob/main/results_2024-01-13T20-20-55.847857.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
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"acc_stderr": 0.033523024960411534,
"acc_norm": 0.5714502762424973,
"acc_norm_stderr": 0.034221321166461816,
"mc1": 0.3463892288861689,
"mc1_stderr": 0.016656997109125136,
"mc2": 0.5143942772336377,
"mc2_stderr": 0.015015865193028501
},
"harness|arc:challenge|25": {
"acc": 0.5861774744027304,
"acc_stderr": 0.014392730009221009,
"acc_norm": 0.6228668941979523,
"acc_norm_stderr": 0.014163366896192598
},
"harness|hellaswag|10": {
"acc": 0.638020314678351,
"acc_stderr": 0.004795908282584543,
"acc_norm": 0.8386775542720574,
"acc_norm_stderr": 0.003670763673792967
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-anatomy|5": {
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"acc_norm": 0.4740740740740741,
"acc_norm_stderr": 0.04313531696750574
},
"harness|hendrycksTest-astronomy|5": {
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"acc_norm_stderr": 0.040335656678483205
},
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},
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"acc_norm": 0.6075471698113207,
"acc_norm_stderr": 0.03005258057955785
},
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},
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},
"harness|hendrycksTest-college_computer_science|5": {
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"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-college_mathematics|5": {
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"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-college_medicine|5": {
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"acc_norm_stderr": 0.03809342081273957
},
"harness|hendrycksTest-college_physics|5": {
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"acc_norm": 0.22549019607843138,
"acc_norm_stderr": 0.041583075330832865
},
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"acc_norm": 0.7,
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},
"harness|hendrycksTest-conceptual_physics|5": {
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},
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"acc_norm": 0.2894736842105263,
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},
"harness|hendrycksTest-electrical_engineering|5": {
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},
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},
"harness|hendrycksTest-formal_logic|5": {
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},
"harness|hendrycksTest-global_facts|5": {
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"acc_norm": 0.35,
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},
"harness|hendrycksTest-high_school_biology|5": {
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},
"harness|hendrycksTest-high_school_chemistry|5": {
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},
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},
"harness|hendrycksTest-high_school_european_history|5": {
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},
"harness|hendrycksTest-high_school_geography|5": {
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"harness|hendrycksTest-high_school_government_and_politics|5": {
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},
"harness|hendrycksTest-high_school_macroeconomics|5": {
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},
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},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.5714285714285714,
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"acc_norm": 0.5714285714285714,
"acc_norm_stderr": 0.032145368597886394
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.31125827814569534,
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"acc_norm": 0.31125827814569534,
"acc_norm_stderr": 0.03780445850526733
},
"harness|hendrycksTest-high_school_psychology|5": {
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"acc_norm_stderr": 0.018861885021534734
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.4074074074074074,
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"acc_norm": 0.4074074074074074,
"acc_norm_stderr": 0.03350991604696042
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7794117647058824,
"acc_stderr": 0.02910225438967408,
"acc_norm": 0.7794117647058824,
"acc_norm_stderr": 0.02910225438967408
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7763713080168776,
"acc_stderr": 0.027123298205229966,
"acc_norm": 0.7763713080168776,
"acc_norm_stderr": 0.027123298205229966
},
"harness|hendrycksTest-human_aging|5": {
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},
"harness|hendrycksTest-human_sexuality|5": {
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},
"harness|hendrycksTest-international_law|5": {
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},
"harness|hendrycksTest-jurisprudence|5": {
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"acc_norm_stderr": 0.04186091791394607
},
"harness|hendrycksTest-logical_fallacies|5": {
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},
"harness|hendrycksTest-machine_learning|5": {
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},
"harness|hendrycksTest-management|5": {
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},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8205128205128205,
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},
"harness|hendrycksTest-medical_genetics|5": {
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},
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"harness|hendrycksTest-moral_disputes|5": {
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"harness|hendrycksTest-moral_scenarios|5": {
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},
"harness|hendrycksTest-nutrition|5": {
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},
"harness|hendrycksTest-philosophy|5": {
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"harness|hendrycksTest-prehistory|5": {
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},
"harness|hendrycksTest-professional_accounting|5": {
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},
"harness|hendrycksTest-professional_law|5": {
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},
"harness|hendrycksTest-professional_medicine|5": {
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},
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},
"harness|hendrycksTest-public_relations|5": {
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},
"harness|hendrycksTest-us_foreign_policy|5": {
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"acc_norm": 0.83,
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},
"harness|hendrycksTest-virology|5": {
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"acc_norm_stderr": 0.03882310850890593
},
"harness|hendrycksTest-world_religions|5": {
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"acc_norm": 0.7660818713450293,
"acc_norm_stderr": 0.03246721765117826
},
"harness|truthfulqa:mc|0": {
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"mc2": 0.5143942772336377,
"mc2_stderr": 0.015015865193028501
},
"harness|winogrande|5": {
"acc": 0.7703235990528808,
"acc_stderr": 0.011821645601838234
},
"harness|gsm8k|5": {
"acc": 0.30401819560272936,
"acc_stderr": 0.012670420440198662
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_diffnamehard__Psyfighter2-Noromaid-ties-Capybara-13B | [
"region:us"
] | 2024-01-13T20:23:14+00:00 | {"pretty_name": "Evaluation run of diffnamehard/Psyfighter2-Noromaid-ties-Capybara-13B", "dataset_summary": "Dataset automatically created during the evaluation run of model [diffnamehard/Psyfighter2-Noromaid-ties-Capybara-13B](https://huggingface.co/diffnamehard/Psyfighter2-Noromaid-ties-Capybara-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_diffnamehard__Psyfighter2-Noromaid-ties-Capybara-13B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T20:20:55.847857](https://huggingface.co/datasets/open-llm-leaderboard/details_diffnamehard__Psyfighter2-Noromaid-ties-Capybara-13B/blob/main/results_2024-01-13T20-20-55.847857.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.5664354333372791,\n \"acc_stderr\": 0.033523024960411534,\n \"acc_norm\": 0.5714502762424973,\n \"acc_norm_stderr\": 0.034221321166461816,\n \"mc1\": 0.3463892288861689,\n \"mc1_stderr\": 0.016656997109125136,\n \"mc2\": 0.5143942772336377,\n \"mc2_stderr\": 0.015015865193028501\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5861774744027304,\n \"acc_stderr\": 0.014392730009221009,\n \"acc_norm\": 0.6228668941979523,\n \"acc_norm_stderr\": 0.014163366896192598\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.638020314678351,\n \"acc_stderr\": 0.004795908282584543,\n \"acc_norm\": 0.8386775542720574,\n \"acc_norm_stderr\": 0.003670763673792967\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4740740740740741,\n \"acc_stderr\": 0.04313531696750574,\n \"acc_norm\": 0.4740740740740741,\n \"acc_norm_stderr\": 0.04313531696750574\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.5657894736842105,\n \"acc_stderr\": 0.040335656678483205,\n \"acc_norm\": 0.5657894736842105,\n \"acc_norm_stderr\": 0.040335656678483205\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.55,\n \"acc_stderr\": 0.04999999999999999,\n \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.04999999999999999\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6075471698113207,\n \"acc_stderr\": 0.03005258057955785,\n \"acc_norm\": 0.6075471698113207,\n \"acc_norm_stderr\": 0.03005258057955785\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6111111111111112,\n \"acc_stderr\": 0.04076663253918567,\n \"acc_norm\": 0.6111111111111112,\n \"acc_norm_stderr\": 0.04076663253918567\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5202312138728323,\n \"acc_stderr\": 0.03809342081273957,\n \"acc_norm\": 0.5202312138728323,\n \"acc_norm_stderr\": 0.03809342081273957\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.041583075330832865,\n \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.041583075330832865\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.4595744680851064,\n \"acc_stderr\": 0.032579014820998356,\n \"acc_norm\": 0.4595744680851064,\n \"acc_norm_stderr\": 0.032579014820998356\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2894736842105263,\n \"acc_stderr\": 0.04266339443159394,\n \"acc_norm\": 0.2894736842105263,\n \"acc_norm_stderr\": 0.04266339443159394\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.496551724137931,\n \"acc_stderr\": 0.041665675771015785,\n \"acc_norm\": 0.496551724137931,\n \"acc_norm_stderr\": 0.041665675771015785\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.3253968253968254,\n \"acc_stderr\": 0.024130158299762613,\n \"acc_norm\": 0.3253968253968254,\n \"acc_norm_stderr\": 0.024130158299762613\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3492063492063492,\n \"acc_stderr\": 0.04263906892795132,\n \"acc_norm\": 0.3492063492063492,\n \"acc_norm_stderr\": 0.04263906892795132\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6709677419354839,\n \"acc_stderr\": 0.02672949906834996,\n \"acc_norm\": 0.6709677419354839,\n \"acc_norm_stderr\": 0.02672949906834996\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.42857142857142855,\n \"acc_stderr\": 0.03481904844438804,\n \"acc_norm\": 0.42857142857142855,\n \"acc_norm_stderr\": 0.03481904844438804\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.59,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.6848484848484848,\n \"acc_stderr\": 0.0362773057502241,\n \"acc_norm\": 0.6848484848484848,\n \"acc_norm_stderr\": 0.0362773057502241\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7171717171717171,\n \"acc_stderr\": 0.03208779558786752,\n \"acc_norm\": 0.7171717171717171,\n \"acc_norm_stderr\": 0.03208779558786752\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8082901554404145,\n \"acc_stderr\": 0.02840895362624526,\n \"acc_norm\": 0.8082901554404145,\n \"acc_norm_stderr\": 0.02840895362624526\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.5307692307692308,\n \"acc_stderr\": 0.025302958890850154,\n \"acc_norm\": 0.5307692307692308,\n \"acc_norm_stderr\": 0.025302958890850154\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.31851851851851853,\n \"acc_stderr\": 0.02840653309060846,\n \"acc_norm\": 0.31851851851851853,\n \"acc_norm_stderr\": 0.02840653309060846\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.5714285714285714,\n \"acc_stderr\": 0.032145368597886394,\n \"acc_norm\": 0.5714285714285714,\n \"acc_norm_stderr\": 0.032145368597886394\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.31125827814569534,\n \"acc_stderr\": 0.03780445850526733,\n \"acc_norm\": 0.31125827814569534,\n \"acc_norm_stderr\": 0.03780445850526733\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.7376146788990826,\n \"acc_stderr\": 0.018861885021534734,\n \"acc_norm\": 0.7376146788990826,\n \"acc_norm_stderr\": 0.018861885021534734\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.4074074074074074,\n \"acc_stderr\": 0.03350991604696042,\n \"acc_norm\": 0.4074074074074074,\n \"acc_norm_stderr\": 0.03350991604696042\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.7794117647058824,\n \"acc_stderr\": 0.02910225438967408,\n \"acc_norm\": 0.7794117647058824,\n \"acc_norm_stderr\": 0.02910225438967408\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7763713080168776,\n \"acc_stderr\": 0.027123298205229966,\n \"acc_norm\": 0.7763713080168776,\n \"acc_norm_stderr\": 0.027123298205229966\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6860986547085202,\n \"acc_stderr\": 0.03114679648297246,\n \"acc_norm\": 0.6860986547085202,\n \"acc_norm_stderr\": 0.03114679648297246\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.6335877862595419,\n \"acc_stderr\": 0.04225875451969638,\n \"acc_norm\": 0.6335877862595419,\n \"acc_norm_stderr\": 0.04225875451969638\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070415,\n \"acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070415\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.6809815950920245,\n \"acc_stderr\": 0.03661997551073836,\n \"acc_norm\": 0.6809815950920245,\n \"acc_norm_stderr\": 0.03661997551073836\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.375,\n \"acc_stderr\": 0.04595091388086298,\n \"acc_norm\": 0.375,\n \"acc_norm_stderr\": 0.04595091388086298\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7184466019417476,\n \"acc_stderr\": 0.044532548363264673,\n \"acc_norm\": 0.7184466019417476,\n \"acc_norm_stderr\": 0.044532548363264673\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8205128205128205,\n \"acc_stderr\": 0.02514093595033544,\n \"acc_norm\": 0.8205128205128205,\n \"acc_norm_stderr\": 0.02514093595033544\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.57,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7484035759897829,\n \"acc_stderr\": 0.015517322365529638,\n \"acc_norm\": 0.7484035759897829,\n \"acc_norm_stderr\": 0.015517322365529638\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.6329479768786127,\n \"acc_stderr\": 0.025950054337654075,\n \"acc_norm\": 0.6329479768786127,\n \"acc_norm_stderr\": 0.025950054337654075\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.46256983240223465,\n \"acc_stderr\": 0.016675578687308082,\n \"acc_norm\": 0.46256983240223465,\n \"acc_norm_stderr\": 0.016675578687308082\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.6372549019607843,\n \"acc_stderr\": 0.027530078447110307,\n \"acc_norm\": 0.6372549019607843,\n \"acc_norm_stderr\": 0.027530078447110307\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6495176848874598,\n \"acc_stderr\": 0.02709865262130175,\n \"acc_norm\": 0.6495176848874598,\n \"acc_norm_stderr\": 0.02709865262130175\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.6234567901234568,\n \"acc_stderr\": 0.02695934451874778,\n \"acc_norm\": 0.6234567901234568,\n \"acc_norm_stderr\": 0.02695934451874778\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.4219858156028369,\n \"acc_stderr\": 0.029462189233370593,\n \"acc_norm\": 0.4219858156028369,\n \"acc_norm_stderr\": 0.029462189233370593\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4406779661016949,\n \"acc_stderr\": 0.012680037994097074,\n \"acc_norm\": 0.4406779661016949,\n \"acc_norm_stderr\": 0.012680037994097074\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.5367647058823529,\n \"acc_stderr\": 0.03029061918048569,\n \"acc_norm\": 0.5367647058823529,\n \"acc_norm_stderr\": 0.03029061918048569\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.5669934640522876,\n \"acc_stderr\": 0.020045442473324224,\n \"acc_norm\": 0.5669934640522876,\n \"acc_norm_stderr\": 0.020045442473324224\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6272727272727273,\n \"acc_stderr\": 0.04631381319425464,\n \"acc_norm\": 0.6272727272727273,\n \"acc_norm_stderr\": 0.04631381319425464\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.6530612244897959,\n \"acc_stderr\": 0.030472526026726496,\n \"acc_norm\": 0.6530612244897959,\n \"acc_norm_stderr\": 0.030472526026726496\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7711442786069652,\n \"acc_stderr\": 0.029705284056772436,\n \"acc_norm\": 0.7711442786069652,\n \"acc_norm_stderr\": 0.029705284056772436\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.83,\n \"acc_stderr\": 0.03775251680686371,\n \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.03775251680686371\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.463855421686747,\n \"acc_stderr\": 0.03882310850890593,\n \"acc_norm\": 0.463855421686747,\n \"acc_norm_stderr\": 0.03882310850890593\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.7660818713450293,\n \"acc_stderr\": 0.03246721765117826,\n \"acc_norm\": 0.7660818713450293,\n \"acc_norm_stderr\": 0.03246721765117826\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3463892288861689,\n \"mc1_stderr\": 0.016656997109125136,\n \"mc2\": 0.5143942772336377,\n \"mc2_stderr\": 0.015015865193028501\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7703235990528808,\n \"acc_stderr\": 0.011821645601838234\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.30401819560272936,\n \"acc_stderr\": 0.012670420440198662\n }\n}\n```", "repo_url": "https://huggingface.co/diffnamehard/Psyfighter2-Noromaid-ties-Capybara-13B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2024_01_13T20_20_55.847857", "path": ["**/details_harness|arc:challenge|25_2024-01-13T20-20-55.847857.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-01-13T20-20-55.847857.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_01_13T20_20_55.847857", "path": ["**/details_harness|gsm8k|5_2024-01-13T20-20-55.847857.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-01-13T20-20-55.847857.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_01_13T20_20_55.847857", "path": ["**/details_harness|hellaswag|10_2024-01-13T20-20-55.847857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-01-13T20-20-55.847857.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_01_13T20_20_55.847857", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T20-20-55.847857.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-01-13T20-20-55.847857.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-01-13T20-20-55.847857.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T20-20-55.847857.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T20-20-55.847857.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-01-13T20-20-55.847857.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T20-20-55.847857.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T20-20-55.847857.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T20-20-55.847857.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T20-20-55.847857.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-01-13T20-20-55.847857.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-01-13T20-20-55.847857.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T20-20-55.847857.parquet", "**/details_harness|hendrycksTest-econometrics|5_2024-01-13T20-20-55.847857.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T20-20-55.847857.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T20-20-55.847857.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T20-20-55.847857.parquet", "**/details_harness|hendrycksTest-global_facts|5_2024-01-13T20-20-55.847857.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T20-20-55.847857.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T20-20-55.847857.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T20-20-55.847857.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-13T20-20-55.847857.parquet", 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"harness_hendrycksTest_high_school_world_history_5", "data_files": [{"split": "2024_01_13T20_20_55.847857", "path": ["**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T20-20-55.847857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T20-20-55.847857.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_aging_5", "data_files": [{"split": "2024_01_13T20_20_55.847857", "path": ["**/details_harness|hendrycksTest-human_aging|5_2024-01-13T20-20-55.847857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-human_aging|5_2024-01-13T20-20-55.847857.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_sexuality_5", "data_files": [{"split": "2024_01_13T20_20_55.847857", "path": ["**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T20-20-55.847857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T20-20-55.847857.parquet"]}]}, {"config_name": "harness_hendrycksTest_international_law_5", "data_files": [{"split": "2024_01_13T20_20_55.847857", "path": ["**/details_harness|hendrycksTest-international_law|5_2024-01-13T20-20-55.847857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-international_law|5_2024-01-13T20-20-55.847857.parquet"]}]}, {"config_name": "harness_hendrycksTest_jurisprudence_5", "data_files": [{"split": "2024_01_13T20_20_55.847857", "path": ["**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T20-20-55.847857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T20-20-55.847857.parquet"]}]}, {"config_name": "harness_hendrycksTest_logical_fallacies_5", "data_files": [{"split": "2024_01_13T20_20_55.847857", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T20-20-55.847857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T20-20-55.847857.parquet"]}]}, {"config_name": "harness_hendrycksTest_machine_learning_5", "data_files": [{"split": "2024_01_13T20_20_55.847857", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T20-20-55.847857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T20-20-55.847857.parquet"]}]}, {"config_name": "harness_hendrycksTest_management_5", "data_files": [{"split": "2024_01_13T20_20_55.847857", "path": ["**/details_harness|hendrycksTest-management|5_2024-01-13T20-20-55.847857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-management|5_2024-01-13T20-20-55.847857.parquet"]}]}, {"config_name": "harness_hendrycksTest_marketing_5", "data_files": [{"split": "2024_01_13T20_20_55.847857", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-13T20-20-55.847857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-13T20-20-55.847857.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2024_01_13T20_20_55.847857", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T20-20-55.847857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T20-20-55.847857.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2024_01_13T20_20_55.847857", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T20-20-55.847857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T20-20-55.847857.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2024_01_13T20_20_55.847857", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T20-20-55.847857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T20-20-55.847857.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2024_01_13T20_20_55.847857", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T20-20-55.847857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T20-20-55.847857.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2024_01_13T20_20_55.847857", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T20-20-55.847857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T20-20-55.847857.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2024_01_13T20_20_55.847857", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T20-20-55.847857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T20-20-55.847857.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2024_01_13T20_20_55.847857", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T20-20-55.847857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T20-20-55.847857.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2024_01_13T20_20_55.847857", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T20-20-55.847857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T20-20-55.847857.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2024_01_13T20_20_55.847857", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T20-20-55.847857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T20-20-55.847857.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_01_13T20_20_55.847857", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T20-20-55.847857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T20-20-55.847857.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_01_13T20_20_55.847857", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T20-20-55.847857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T20-20-55.847857.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_01_13T20_20_55.847857", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T20-20-55.847857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T20-20-55.847857.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_01_13T20_20_55.847857", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T20-20-55.847857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T20-20-55.847857.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_01_13T20_20_55.847857", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T20-20-55.847857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T20-20-55.847857.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_01_13T20_20_55.847857", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T20-20-55.847857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T20-20-55.847857.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_13T20_20_55.847857", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T20-20-55.847857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T20-20-55.847857.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_13T20_20_55.847857", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T20-20-55.847857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T20-20-55.847857.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_13T20_20_55.847857", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T20-20-55.847857.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T20-20-55.847857.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T20_20_55.847857", "path": ["**/details_harness|winogrande|5_2024-01-13T20-20-55.847857.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T20-20-55.847857.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T20_20_55.847857", "path": ["results_2024-01-13T20-20-55.847857.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T20-20-55.847857.parquet"]}]}]} | 2024-01-13T20:23:34+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of diffnamehard/Psyfighter2-Noromaid-ties-Capybara-13B
Dataset automatically created during the evaluation run of model diffnamehard/Psyfighter2-Noromaid-ties-Capybara-13B on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-13T20:20:55.847857(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of diffnamehard/Psyfighter2-Noromaid-ties-Capybara-13B\n\n\n\nDataset automatically created during the evaluation run of model diffnamehard/Psyfighter2-Noromaid-ties-Capybara-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T20:20:55.847857(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of diffnamehard/Psyfighter2-Noromaid-ties-Capybara-13B\n\n\n\nDataset automatically created during the evaluation run of model diffnamehard/Psyfighter2-Noromaid-ties-Capybara-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T20:20:55.847857(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
41d618511ea3f3a3b472a3bafd90a8a3913e3b34 |
# Dataset Card for Evaluation run of HenryJJ/dolphin-2.6-mistral-7b-dpo-orca
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [HenryJJ/dolphin-2.6-mistral-7b-dpo-orca](https://huggingface.co/HenryJJ/dolphin-2.6-mistral-7b-dpo-orca) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_HenryJJ__dolphin-2.6-mistral-7b-dpo-orca",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T20:29:58.885355](https://huggingface.co/datasets/open-llm-leaderboard/details_HenryJJ__dolphin-2.6-mistral-7b-dpo-orca/blob/main/results_2024-01-13T20-29-58.885355.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6191660640057981,
"acc_stderr": 0.03263652891344978,
"acc_norm": 0.6271945727055741,
"acc_norm_stderr": 0.03333445432068468,
"mc1": 0.43329253365973075,
"mc1_stderr": 0.017347024450107492,
"mc2": 0.5997212380160826,
"mc2_stderr": 0.015696061571327326
},
"harness|arc:challenge|25": {
"acc": 0.6271331058020477,
"acc_stderr": 0.014131176760131167,
"acc_norm": 0.6604095563139932,
"acc_norm_stderr": 0.01383903976282017
},
"harness|hellaswag|10": {
"acc": 0.6580362477594105,
"acc_stderr": 0.004733980470799212,
"acc_norm": 0.8462457677753435,
"acc_norm_stderr": 0.0035997580435468044
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.29,
"acc_stderr": 0.04560480215720684,
"acc_norm": 0.29,
"acc_norm_stderr": 0.04560480215720684
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6444444444444445,
"acc_stderr": 0.04135176749720385,
"acc_norm": 0.6444444444444445,
"acc_norm_stderr": 0.04135176749720385
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6513157894736842,
"acc_stderr": 0.0387813988879761,
"acc_norm": 0.6513157894736842,
"acc_norm_stderr": 0.0387813988879761
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.58,
"acc_stderr": 0.04960449637488583,
"acc_norm": 0.58,
"acc_norm_stderr": 0.04960449637488583
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6490566037735849,
"acc_stderr": 0.02937364625323469,
"acc_norm": 0.6490566037735849,
"acc_norm_stderr": 0.02937364625323469
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7222222222222222,
"acc_stderr": 0.037455547914624555,
"acc_norm": 0.7222222222222222,
"acc_norm_stderr": 0.037455547914624555
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.52,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.52,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
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"harness|hendrycksTest-us_foreign_policy|5": {
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}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Who are the source data producers?
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#### Annotation process
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#### Who are the annotators?
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#### Personal and Sensitive Information
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## Bias, Risks, and Limitations
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_HenryJJ__dolphin-2.6-mistral-7b-dpo-orca | [
"region:us"
] | 2024-01-13T20:32:17+00:00 | {"pretty_name": "Evaluation run of HenryJJ/dolphin-2.6-mistral-7b-dpo-orca", "dataset_summary": "Dataset automatically created during the evaluation run of model [HenryJJ/dolphin-2.6-mistral-7b-dpo-orca](https://huggingface.co/HenryJJ/dolphin-2.6-mistral-7b-dpo-orca) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_HenryJJ__dolphin-2.6-mistral-7b-dpo-orca\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T20:29:58.885355](https://huggingface.co/datasets/open-llm-leaderboard/details_HenryJJ__dolphin-2.6-mistral-7b-dpo-orca/blob/main/results_2024-01-13T20-29-58.885355.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6191660640057981,\n \"acc_stderr\": 0.03263652891344978,\n \"acc_norm\": 0.6271945727055741,\n \"acc_norm_stderr\": 0.03333445432068468,\n \"mc1\": 0.43329253365973075,\n \"mc1_stderr\": 0.017347024450107492,\n \"mc2\": 0.5997212380160826,\n \"mc2_stderr\": 0.015696061571327326\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6271331058020477,\n \"acc_stderr\": 0.014131176760131167,\n \"acc_norm\": 0.6604095563139932,\n \"acc_norm_stderr\": 0.01383903976282017\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6580362477594105,\n \"acc_stderr\": 0.004733980470799212,\n \"acc_norm\": 0.8462457677753435,\n \"acc_norm_stderr\": 0.0035997580435468044\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6444444444444445,\n \"acc_stderr\": 0.04135176749720385,\n \"acc_norm\": 0.6444444444444445,\n \"acc_norm_stderr\": 0.04135176749720385\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6513157894736842,\n \"acc_stderr\": 0.0387813988879761,\n \"acc_norm\": 0.6513157894736842,\n \"acc_norm_stderr\": 0.0387813988879761\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.58,\n \"acc_stderr\": 0.04960449637488583,\n \"acc_norm\": 0.58,\n \"acc_norm_stderr\": 0.04960449637488583\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6490566037735849,\n \"acc_stderr\": 0.02937364625323469,\n \"acc_norm\": 0.6490566037735849,\n \"acc_norm_stderr\": 0.02937364625323469\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.037455547914624555,\n \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.037455547914624555\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5895953757225434,\n \"acc_stderr\": 0.03750757044895537,\n \"acc_norm\": 0.5895953757225434,\n \"acc_norm_stderr\": 0.03750757044895537\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.04897104952726366,\n \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.04897104952726366\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.77,\n \"acc_stderr\": 0.042295258468165065,\n 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"path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T20-29-58.885355.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2024_01_13T20_29_58.885355", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T20-29-58.885355.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T20-29-58.885355.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2024_01_13T20_29_58.885355", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T20-29-58.885355.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T20-29-58.885355.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2024_01_13T20_29_58.885355", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T20-29-58.885355.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T20-29-58.885355.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2024_01_13T20_29_58.885355", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T20-29-58.885355.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T20-29-58.885355.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2024_01_13T20_29_58.885355", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T20-29-58.885355.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T20-29-58.885355.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2024_01_13T20_29_58.885355", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T20-29-58.885355.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T20-29-58.885355.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_01_13T20_29_58.885355", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T20-29-58.885355.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T20-29-58.885355.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_01_13T20_29_58.885355", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T20-29-58.885355.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T20-29-58.885355.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_01_13T20_29_58.885355", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T20-29-58.885355.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T20-29-58.885355.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_01_13T20_29_58.885355", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T20-29-58.885355.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T20-29-58.885355.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_01_13T20_29_58.885355", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T20-29-58.885355.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T20-29-58.885355.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_01_13T20_29_58.885355", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T20-29-58.885355.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T20-29-58.885355.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_13T20_29_58.885355", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T20-29-58.885355.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T20-29-58.885355.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_13T20_29_58.885355", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T20-29-58.885355.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T20-29-58.885355.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_13T20_29_58.885355", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T20-29-58.885355.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T20-29-58.885355.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T20_29_58.885355", "path": ["**/details_harness|winogrande|5_2024-01-13T20-29-58.885355.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T20-29-58.885355.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T20_29_58.885355", "path": ["results_2024-01-13T20-29-58.885355.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T20-29-58.885355.parquet"]}]}]} | 2024-01-13T20:32:38+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of HenryJJ/dolphin-2.6-mistral-7b-dpo-orca
Dataset automatically created during the evaluation run of model HenryJJ/dolphin-2.6-mistral-7b-dpo-orca on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-13T20:29:58.885355(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of HenryJJ/dolphin-2.6-mistral-7b-dpo-orca\n\n\n\nDataset automatically created during the evaluation run of model HenryJJ/dolphin-2.6-mistral-7b-dpo-orca on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T20:29:58.885355(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of HenryJJ/dolphin-2.6-mistral-7b-dpo-orca\n\n\n\nDataset automatically created during the evaluation run of model HenryJJ/dolphin-2.6-mistral-7b-dpo-orca on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T20:29:58.885355(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
ab9ed84fe9ee8fb420f99b8ccffb52d74184ddfe |
# Dataset Card for Evaluation run of RatanRohith/NeuralPizza-7B-V0.1
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [RatanRohith/NeuralPizza-7B-V0.1](https://huggingface.co/RatanRohith/NeuralPizza-7B-V0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_RatanRohith__NeuralPizza-7B-V0.1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T20:34:27.461906](https://huggingface.co/datasets/open-llm-leaderboard/details_RatanRohith__NeuralPizza-7B-V0.1/blob/main/results_2024-01-13T20-34-27.461906.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6475482692691918,
"acc_stderr": 0.03229281059357549,
"acc_norm": 0.64912528215574,
"acc_norm_stderr": 0.03294231215287106,
"mc1": 0.5042839657282742,
"mc1_stderr": 0.017502858577371255,
"mc2": 0.6721952166431592,
"mc2_stderr": 0.015433999381498234
},
"harness|arc:challenge|25": {
"acc": 0.6757679180887372,
"acc_stderr": 0.013678810399518822,
"acc_norm": 0.7047781569965871,
"acc_norm_stderr": 0.01332975029338232
},
"harness|hellaswag|10": {
"acc": 0.7062338179645489,
"acc_stderr": 0.004545552424153379,
"acc_norm": 0.8730332603067118,
"acc_norm_stderr": 0.0033225528296089036
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6296296296296297,
"acc_stderr": 0.041716541613545426,
"acc_norm": 0.6296296296296297,
"acc_norm_stderr": 0.041716541613545426
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6710526315789473,
"acc_stderr": 0.03823428969926605,
"acc_norm": 0.6710526315789473,
"acc_norm_stderr": 0.03823428969926605
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.61,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.61,
"acc_norm_stderr": 0.04902071300001975
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7132075471698113,
"acc_stderr": 0.027834912527544067,
"acc_norm": 0.7132075471698113,
"acc_norm_stderr": 0.027834912527544067
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7638888888888888,
"acc_stderr": 0.03551446610810826,
"acc_norm": 0.7638888888888888,
"acc_norm_stderr": 0.03551446610810826
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.47,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.47,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620333
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6763005780346821,
"acc_stderr": 0.0356760379963917,
"acc_norm": 0.6763005780346821,
"acc_norm_stderr": 0.0356760379963917
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4411764705882353,
"acc_stderr": 0.049406356306056595,
"acc_norm": 0.4411764705882353,
"acc_norm_stderr": 0.049406356306056595
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5914893617021276,
"acc_stderr": 0.032134180267015755,
"acc_norm": 0.5914893617021276,
"acc_norm_stderr": 0.032134180267015755
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4824561403508772,
"acc_stderr": 0.04700708033551038,
"acc_norm": 0.4824561403508772,
"acc_norm_stderr": 0.04700708033551038
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5172413793103449,
"acc_stderr": 0.04164188720169375,
"acc_norm": 0.5172413793103449,
"acc_norm_stderr": 0.04164188720169375
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.38095238095238093,
"acc_stderr": 0.02501074911613759,
"acc_norm": 0.38095238095238093,
"acc_norm_stderr": 0.02501074911613759
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.42857142857142855,
"acc_stderr": 0.0442626668137991,
"acc_norm": 0.42857142857142855,
"acc_norm_stderr": 0.0442626668137991
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.38,
"acc_stderr": 0.048783173121456316,
"acc_norm": 0.38,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7709677419354839,
"acc_stderr": 0.023904914311782648,
"acc_norm": 0.7709677419354839,
"acc_norm_stderr": 0.023904914311782648
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5172413793103449,
"acc_stderr": 0.035158955511656986,
"acc_norm": 0.5172413793103449,
"acc_norm_stderr": 0.035158955511656986
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.7,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7515151515151515,
"acc_stderr": 0.033744026441394036,
"acc_norm": 0.7515151515151515,
"acc_norm_stderr": 0.033744026441394036
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.02962022787479047,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.02962022787479047
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8911917098445595,
"acc_stderr": 0.022473253332768763,
"acc_norm": 0.8911917098445595,
"acc_norm_stderr": 0.022473253332768763
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.676923076923077,
"acc_stderr": 0.023710888501970572,
"acc_norm": 0.676923076923077,
"acc_norm_stderr": 0.023710888501970572
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.34444444444444444,
"acc_stderr": 0.02897264888484427,
"acc_norm": 0.34444444444444444,
"acc_norm_stderr": 0.02897264888484427
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.7352941176470589,
"acc_stderr": 0.02865749128507197,
"acc_norm": 0.7352941176470589,
"acc_norm_stderr": 0.02865749128507197
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"harness|hendrycksTest-high_school_world_history|5": {
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"harness|hendrycksTest-nutrition|5": {
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"harness|hendrycksTest-security_studies|5": {
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"harness|hendrycksTest-sociology|5": {
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"acc_norm": 0.8656716417910447,
"acc_norm_stderr": 0.02411267824090083
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"harness|hendrycksTest-us_foreign_policy|5": {
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"harness|hendrycksTest-virology|5": {
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"acc_norm": 0.5180722891566265,
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"harness|hendrycksTest-world_religions|5": {
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"harness|winogrande|5": {
"acc": 0.8034727703235991,
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},
"harness|gsm8k|5": {
"acc": 0.5943896891584534,
"acc_stderr": 0.01352484889446211
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Who are the source data producers?
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#### Annotation process
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#### Personal and Sensitive Information
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## Bias, Risks, and Limitations
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_RatanRohith__NeuralPizza-7B-V0.1 | [
"region:us"
] | 2024-01-13T20:36:43+00:00 | {"pretty_name": "Evaluation run of RatanRohith/NeuralPizza-7B-V0.1", "dataset_summary": "Dataset automatically created during the evaluation run of model [RatanRohith/NeuralPizza-7B-V0.1](https://huggingface.co/RatanRohith/NeuralPizza-7B-V0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_RatanRohith__NeuralPizza-7B-V0.1\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T20:34:27.461906](https://huggingface.co/datasets/open-llm-leaderboard/details_RatanRohith__NeuralPizza-7B-V0.1/blob/main/results_2024-01-13T20-34-27.461906.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6475482692691918,\n \"acc_stderr\": 0.03229281059357549,\n \"acc_norm\": 0.64912528215574,\n \"acc_norm_stderr\": 0.03294231215287106,\n \"mc1\": 0.5042839657282742,\n \"mc1_stderr\": 0.017502858577371255,\n \"mc2\": 0.6721952166431592,\n \"mc2_stderr\": 0.015433999381498234\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6757679180887372,\n \"acc_stderr\": 0.013678810399518822,\n \"acc_norm\": 0.7047781569965871,\n \"acc_norm_stderr\": 0.01332975029338232\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7062338179645489,\n \"acc_stderr\": 0.004545552424153379,\n \"acc_norm\": 0.8730332603067118,\n \"acc_norm_stderr\": 0.0033225528296089036\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6296296296296297,\n \"acc_stderr\": 0.041716541613545426,\n \"acc_norm\": 0.6296296296296297,\n \"acc_norm_stderr\": 0.041716541613545426\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6710526315789473,\n \"acc_stderr\": 0.03823428969926605,\n \"acc_norm\": 0.6710526315789473,\n \"acc_norm_stderr\": 0.03823428969926605\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.7132075471698113,\n \"acc_stderr\": 0.027834912527544067,\n \"acc_norm\": 0.7132075471698113,\n \"acc_norm_stderr\": 0.027834912527544067\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7638888888888888,\n \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.7638888888888888,\n \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6763005780346821,\n \"acc_stderr\": 0.0356760379963917,\n \"acc_norm\": 0.6763005780346821,\n \"acc_norm_stderr\": 0.0356760379963917\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.049406356306056595,\n \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.049406356306056595\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5914893617021276,\n \"acc_stderr\": 0.032134180267015755,\n \"acc_norm\": 0.5914893617021276,\n \"acc_norm_stderr\": 0.032134180267015755\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4824561403508772,\n \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.04164188720169375,\n \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.04164188720169375\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.38095238095238093,\n \"acc_stderr\": 0.02501074911613759,\n \"acc_norm\": 0.38095238095238093,\n \"acc_norm_stderr\": 0.02501074911613759\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42857142857142855,\n \"acc_stderr\": 0.0442626668137991,\n \"acc_norm\": 0.42857142857142855,\n \"acc_norm_stderr\": 0.0442626668137991\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7709677419354839,\n \"acc_stderr\": 0.023904914311782648,\n \"acc_norm\": 0.7709677419354839,\n \"acc_norm_stderr\": 0.023904914311782648\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.035158955511656986,\n \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.035158955511656986\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7515151515151515,\n \"acc_stderr\": 0.033744026441394036,\n \"acc_norm\": 0.7515151515151515,\n \"acc_norm_stderr\": 0.033744026441394036\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.02962022787479047,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.02962022787479047\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8911917098445595,\n \"acc_stderr\": 0.022473253332768763,\n \"acc_norm\": 0.8911917098445595,\n \"acc_norm_stderr\": 0.022473253332768763\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.676923076923077,\n \"acc_stderr\": 0.023710888501970572,\n \"acc_norm\": 0.676923076923077,\n \"acc_norm_stderr\": 0.023710888501970572\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.34444444444444444,\n \"acc_stderr\": 0.02897264888484427,\n \"acc_norm\": 0.34444444444444444,\n \"acc_norm_stderr\": 0.02897264888484427\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.7352941176470589,\n \"acc_stderr\": 0.02865749128507197,\n \"acc_norm\": 0.7352941176470589,\n \"acc_norm_stderr\": 0.02865749128507197\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.36423841059602646,\n \"acc_stderr\": 0.03929111781242742,\n \"acc_norm\": 0.36423841059602646,\n \"acc_norm_stderr\": 0.03929111781242742\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8403669724770643,\n \"acc_stderr\": 0.015703498348461766,\n \"acc_norm\": 0.8403669724770643,\n \"acc_norm_stderr\": 0.015703498348461766\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5555555555555556,\n \"acc_stderr\": 0.03388857118502325,\n \"acc_norm\": 0.5555555555555556,\n \"acc_norm_stderr\": 0.03388857118502325\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8235294117647058,\n \"acc_stderr\": 0.026756401538078962,\n \"acc_norm\": 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["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T20-34-27.461906.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T20-34-27.461906.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_01_13T20_34_27.461906", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T20-34-27.461906.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T20-34-27.461906.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_01_13T20_34_27.461906", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T20-34-27.461906.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T20-34-27.461906.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_01_13T20_34_27.461906", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T20-34-27.461906.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T20-34-27.461906.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_13T20_34_27.461906", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T20-34-27.461906.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T20-34-27.461906.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_13T20_34_27.461906", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T20-34-27.461906.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T20-34-27.461906.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_13T20_34_27.461906", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T20-34-27.461906.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T20-34-27.461906.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T20_34_27.461906", "path": ["**/details_harness|winogrande|5_2024-01-13T20-34-27.461906.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T20-34-27.461906.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T20_34_27.461906", "path": ["results_2024-01-13T20-34-27.461906.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T20-34-27.461906.parquet"]}]}]} | 2024-01-13T20:37:04+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of RatanRohith/NeuralPizza-7B-V0.1
Dataset automatically created during the evaluation run of model RatanRohith/NeuralPizza-7B-V0.1 on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-13T20:34:27.461906(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of RatanRohith/NeuralPizza-7B-V0.1\n\n\n\nDataset automatically created during the evaluation run of model RatanRohith/NeuralPizza-7B-V0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T20:34:27.461906(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of RatanRohith/NeuralPizza-7B-V0.1\n\n\n\nDataset automatically created during the evaluation run of model RatanRohith/NeuralPizza-7B-V0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T20:34:27.461906(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
20bb86190831686862fd9433bb417c5f1a2506d6 | The dataset contains multi-modal data from over 75,000 open access and de-identified case reports, including metadata, clinical cases, image captions and more than 130,000 images. Images and clinical cases belong to different medical specialties, such as oncology, cardiology, surgery and pathology. The structure of the dataset allows to easily map images with their corresponding article metadata, clinical case, captions and image labels. Details of the data structure can be found in the file data_dictionary.csv.
Almost 100,000 patients and almost 400,000 medical doctors and researchers were involved in the creation of the articles included in this dataset. The citation data of each article can be found in the metadata.parquet file.
Refer to the examples showcased in [this GitHub repository](https://github.com/mauro-nievoff/MultiCaRe_Dataset) to understand how to optimize the use of this dataset.
For a detailed insight about the contents of this dataset, please refer to [this data article](https://www.sciencedirect.com/science/article/pii/S2352340923010351) published in Data In Brief.
The dataset is also available on [Zenodo](https://zenodo.org/records/10079370). | mauro-nievoff/MultiCaRe_Dataset | [
"task_categories:image-classification",
"task_categories:image-to-text",
"task_categories:text-to-image",
"language:en",
"license:cc-by-4.0",
"medical",
"images",
"computer vision",
"multimodal",
"text",
"clinical",
"nlp",
"region:us"
] | 2024-01-13T20:38:21+00:00 | {"language": ["en"], "license": "cc-by-4.0", "task_categories": ["image-classification", "image-to-text", "text-to-image"], "pretty_name": "MultiCaRe Dataset", "tags": ["medical", "images", "computer vision", "multimodal", "text", "clinical", "nlp"]} | 2024-01-14T15:02:24+00:00 | [] | [
"en"
] | TAGS
#task_categories-image-classification #task_categories-image-to-text #task_categories-text-to-image #language-English #license-cc-by-4.0 #medical #images #computer vision #multimodal #text #clinical #nlp #region-us
| The dataset contains multi-modal data from over 75,000 open access and de-identified case reports, including metadata, clinical cases, image captions and more than 130,000 images. Images and clinical cases belong to different medical specialties, such as oncology, cardiology, surgery and pathology. The structure of the dataset allows to easily map images with their corresponding article metadata, clinical case, captions and image labels. Details of the data structure can be found in the file data_dictionary.csv.
Almost 100,000 patients and almost 400,000 medical doctors and researchers were involved in the creation of the articles included in this dataset. The citation data of each article can be found in the metadata.parquet file.
Refer to the examples showcased in this GitHub repository to understand how to optimize the use of this dataset.
For a detailed insight about the contents of this dataset, please refer to this data article published in Data In Brief.
The dataset is also available on Zenodo. | [] | [
"TAGS\n#task_categories-image-classification #task_categories-image-to-text #task_categories-text-to-image #language-English #license-cc-by-4.0 #medical #images #computer vision #multimodal #text #clinical #nlp #region-us \n"
] |
e2dcadae9756394104348ed94b7b580d102e7f94 |
# Dataset Card for Evaluation run of kevin009/lamatama
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [kevin009/lamatama](https://huggingface.co/kevin009/lamatama) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_kevin009__lamatama",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T20:41:45.535254](https://huggingface.co/datasets/open-llm-leaderboard/details_kevin009__lamatama/blob/main/results_2024-01-13T20-41-45.535254.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.25449022748027395,
"acc_stderr": 0.030683186117771787,
"acc_norm": 0.2553066382594162,
"acc_norm_stderr": 0.031421716720794905,
"mc1": 0.23378212974296206,
"mc1_stderr": 0.01481619599193158,
"mc2": 0.3767314036539428,
"mc2_stderr": 0.013774459138435797
},
"harness|arc:challenge|25": {
"acc": 0.34726962457337884,
"acc_stderr": 0.013913034529620436,
"acc_norm": 0.363481228668942,
"acc_norm_stderr": 0.014056207319068283
},
"harness|hellaswag|10": {
"acc": 0.45777733519219277,
"acc_stderr": 0.004971958480920495,
"acc_norm": 0.6112328221469827,
"acc_norm_stderr": 0.004864740134043669
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.21,
"acc_stderr": 0.040936018074033256,
"acc_norm": 0.21,
"acc_norm_stderr": 0.040936018074033256
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.14814814814814814,
"acc_stderr": 0.030688647610352674,
"acc_norm": 0.14814814814814814,
"acc_norm_stderr": 0.030688647610352674
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.17763157894736842,
"acc_stderr": 0.031103182383123387,
"acc_norm": 0.17763157894736842,
"acc_norm_stderr": 0.031103182383123387
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.26,
"acc_stderr": 0.04408440022768079,
"acc_norm": 0.26,
"acc_norm_stderr": 0.04408440022768079
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.2641509433962264,
"acc_stderr": 0.02713429162874171,
"acc_norm": 0.2641509433962264,
"acc_norm_stderr": 0.02713429162874171
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.2222222222222222,
"acc_stderr": 0.03476590104304134,
"acc_norm": 0.2222222222222222,
"acc_norm_stderr": 0.03476590104304134
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.22,
"acc_stderr": 0.0416333199893227,
"acc_norm": 0.22,
"acc_norm_stderr": 0.0416333199893227
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.29,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.29,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.1907514450867052,
"acc_stderr": 0.029957851329869337,
"acc_norm": 0.1907514450867052,
"acc_norm_stderr": 0.029957851329869337
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.17647058823529413,
"acc_stderr": 0.03793281185307811,
"acc_norm": 0.17647058823529413,
"acc_norm_stderr": 0.03793281185307811
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542127,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542127
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.25957446808510637,
"acc_stderr": 0.02865917937429232,
"acc_norm": 0.25957446808510637,
"acc_norm_stderr": 0.02865917937429232
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.21929824561403508,
"acc_stderr": 0.03892431106518754,
"acc_norm": 0.21929824561403508,
"acc_norm_stderr": 0.03892431106518754
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.25517241379310346,
"acc_stderr": 0.03632984052707842,
"acc_norm": 0.25517241379310346,
"acc_norm_stderr": 0.03632984052707842
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.24338624338624337,
"acc_stderr": 0.022101128787415433,
"acc_norm": 0.24338624338624337,
"acc_norm_stderr": 0.022101128787415433
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.23809523809523808,
"acc_stderr": 0.03809523809523809,
"acc_norm": 0.23809523809523808,
"acc_norm_stderr": 0.03809523809523809
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.29,
"acc_stderr": 0.04560480215720684,
"acc_norm": 0.29,
"acc_norm_stderr": 0.04560480215720684
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.22258064516129034,
"acc_stderr": 0.02366421667164251,
"acc_norm": 0.22258064516129034,
"acc_norm_stderr": 0.02366421667164251
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.1921182266009852,
"acc_stderr": 0.02771931570961477,
"acc_norm": 0.1921182266009852,
"acc_norm_stderr": 0.02771931570961477
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.24,
"acc_stderr": 0.04292346959909284,
"acc_norm": 0.24,
"acc_norm_stderr": 0.04292346959909284
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.24242424242424243,
"acc_stderr": 0.033464098810559534,
"acc_norm": 0.24242424242424243,
"acc_norm_stderr": 0.033464098810559534
},
"harness|hendrycksTest-high_school_geography|5": {
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"acc_norm": 0.21212121212121213,
"acc_norm_stderr": 0.02912652283458682
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.21761658031088082,
"acc_stderr": 0.029778663037752943,
"acc_norm": 0.21761658031088082,
"acc_norm_stderr": 0.029778663037752943
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.24102564102564103,
"acc_stderr": 0.02168554666533319,
"acc_norm": 0.24102564102564103,
"acc_norm_stderr": 0.02168554666533319
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.26296296296296295,
"acc_stderr": 0.026842057873833706,
"acc_norm": 0.26296296296296295,
"acc_norm_stderr": 0.026842057873833706
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.226890756302521,
"acc_stderr": 0.02720537153827947,
"acc_norm": 0.226890756302521,
"acc_norm_stderr": 0.02720537153827947
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.2251655629139073,
"acc_stderr": 0.03410435282008936,
"acc_norm": 0.2251655629139073,
"acc_norm_stderr": 0.03410435282008936
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.23302752293577983,
"acc_stderr": 0.0181256691808615,
"acc_norm": 0.23302752293577983,
"acc_norm_stderr": 0.0181256691808615
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.3425925925925926,
"acc_stderr": 0.03236585252602158,
"acc_norm": 0.3425925925925926,
"acc_norm_stderr": 0.03236585252602158
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.24019607843137256,
"acc_stderr": 0.02998373305591362,
"acc_norm": 0.24019607843137256,
"acc_norm_stderr": 0.02998373305591362
},
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"harness|hendrycksTest-virology|5": {
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"harness|hendrycksTest-world_religions|5": {
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"harness|winogrande|5": {
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},
"harness|gsm8k|5": {
"acc": 0.022744503411675512,
"acc_stderr": 0.004106620637749707
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## Dataset Structure
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Annotation process
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#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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## Bias, Risks, and Limitations
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_kevin009__lamatama | [
"region:us"
] | 2024-01-13T20:43:34+00:00 | {"pretty_name": "Evaluation run of kevin009/lamatama", "dataset_summary": "Dataset automatically created during the evaluation run of model [kevin009/lamatama](https://huggingface.co/kevin009/lamatama) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_kevin009__lamatama\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T20:41:45.535254](https://huggingface.co/datasets/open-llm-leaderboard/details_kevin009__lamatama/blob/main/results_2024-01-13T20-41-45.535254.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.25449022748027395,\n \"acc_stderr\": 0.030683186117771787,\n \"acc_norm\": 0.2553066382594162,\n \"acc_norm_stderr\": 0.031421716720794905,\n \"mc1\": 0.23378212974296206,\n \"mc1_stderr\": 0.01481619599193158,\n \"mc2\": 0.3767314036539428,\n \"mc2_stderr\": 0.013774459138435797\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.34726962457337884,\n \"acc_stderr\": 0.013913034529620436,\n \"acc_norm\": 0.363481228668942,\n \"acc_norm_stderr\": 0.014056207319068283\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.45777733519219277,\n \"acc_stderr\": 0.004971958480920495,\n \"acc_norm\": 0.6112328221469827,\n \"acc_norm_stderr\": 0.004864740134043669\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.14814814814814814,\n \"acc_stderr\": 0.030688647610352674,\n \"acc_norm\": 0.14814814814814814,\n \"acc_norm_stderr\": 0.030688647610352674\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.17763157894736842,\n \"acc_stderr\": 0.031103182383123387,\n \"acc_norm\": 0.17763157894736842,\n \"acc_norm_stderr\": 0.031103182383123387\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.2641509433962264,\n \"acc_stderr\": 0.02713429162874171,\n \"acc_norm\": 0.2641509433962264,\n \"acc_norm_stderr\": 0.02713429162874171\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2222222222222222,\n \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.22,\n \"acc_stderr\": 0.0416333199893227,\n \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.0416333199893227\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.1907514450867052,\n \"acc_stderr\": 0.029957851329869337,\n \"acc_norm\": 0.1907514450867052,\n \"acc_norm_stderr\": 0.029957851329869337\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.17647058823529413,\n \"acc_stderr\": 0.03793281185307811,\n \"acc_norm\": 0.17647058823529413,\n \"acc_norm_stderr\": 0.03793281185307811\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.25957446808510637,\n \"acc_stderr\": 0.02865917937429232,\n \"acc_norm\": 0.25957446808510637,\n \"acc_norm_stderr\": 0.02865917937429232\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.21929824561403508,\n \"acc_stderr\": 0.03892431106518754,\n \"acc_norm\": 0.21929824561403508,\n \"acc_norm_stderr\": 0.03892431106518754\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.25517241379310346,\n \"acc_stderr\": 0.03632984052707842,\n \"acc_norm\": 0.25517241379310346,\n \"acc_norm_stderr\": 0.03632984052707842\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.24338624338624337,\n \"acc_stderr\": 0.022101128787415433,\n \"acc_norm\": 0.24338624338624337,\n \"acc_norm_stderr\": 0.022101128787415433\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.23809523809523808,\n \"acc_stderr\": 0.03809523809523809,\n \"acc_norm\": 0.23809523809523808,\n \"acc_norm_stderr\": 0.03809523809523809\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.22258064516129034,\n \"acc_stderr\": 0.02366421667164251,\n \"acc_norm\": 0.22258064516129034,\n \"acc_norm_stderr\": 0.02366421667164251\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.1921182266009852,\n \"acc_stderr\": 0.02771931570961477,\n \"acc_norm\": 0.1921182266009852,\n \"acc_norm_stderr\": 0.02771931570961477\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909284,\n \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909284\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.24242424242424243,\n \"acc_stderr\": 0.033464098810559534,\n \"acc_norm\": 0.24242424242424243,\n \"acc_norm_stderr\": 0.033464098810559534\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.21212121212121213,\n \"acc_stderr\": 0.02912652283458682,\n \"acc_norm\": 0.21212121212121213,\n \"acc_norm_stderr\": 0.02912652283458682\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.21761658031088082,\n \"acc_stderr\": 0.029778663037752943,\n \"acc_norm\": 0.21761658031088082,\n \"acc_norm_stderr\": 0.029778663037752943\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.24102564102564103,\n \"acc_stderr\": 0.02168554666533319,\n \"acc_norm\": 0.24102564102564103,\n \"acc_norm_stderr\": 0.02168554666533319\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.26296296296296295,\n \"acc_stderr\": 0.026842057873833706,\n \"acc_norm\": 0.26296296296296295,\n \"acc_norm_stderr\": 0.026842057873833706\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.226890756302521,\n \"acc_stderr\": 0.02720537153827947,\n \"acc_norm\": 0.226890756302521,\n \"acc_norm_stderr\": 0.02720537153827947\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.2251655629139073,\n \"acc_stderr\": 0.03410435282008936,\n \"acc_norm\": 0.2251655629139073,\n \"acc_norm_stderr\": 0.03410435282008936\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.23302752293577983,\n \"acc_stderr\": 0.0181256691808615,\n \"acc_norm\": 0.23302752293577983,\n \"acc_norm_stderr\": 0.0181256691808615\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.3425925925925926,\n \"acc_stderr\": 0.03236585252602158,\n \"acc_norm\": 0.3425925925925926,\n \"acc_norm_stderr\": 0.03236585252602158\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.24019607843137256,\n \"acc_stderr\": 0.02998373305591362,\n \"acc_norm\": 0.24019607843137256,\n \"acc_norm_stderr\": 0.02998373305591362\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.25316455696202533,\n \"acc_stderr\": 0.028304657943035307,\n \"acc_norm\": 0.25316455696202533,\n \"acc_norm_stderr\": 0.028304657943035307\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.34977578475336324,\n \"acc_stderr\": 0.03200736719484503,\n \"acc_norm\": 0.34977578475336324,\n \"acc_norm_stderr\": 0.03200736719484503\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.22900763358778625,\n \"acc_stderr\": 0.036853466317118506,\n \"acc_norm\": 0.22900763358778625,\n \"acc_norm_stderr\": 0.036853466317118506\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.24793388429752067,\n \"acc_stderr\": 0.039418975265163025,\n \"acc_norm\": 0.24793388429752067,\n \"acc_norm_stderr\": 0.039418975265163025\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.28703703703703703,\n 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["**/details_harness|truthfulqa:mc|0_2024-01-13T20-41-45.535254.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T20-41-45.535254.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T20_41_45.535254", "path": ["**/details_harness|winogrande|5_2024-01-13T20-41-45.535254.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T20-41-45.535254.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T20_41_45.535254", "path": ["results_2024-01-13T20-41-45.535254.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T20-41-45.535254.parquet"]}]}]} | 2024-01-13T20:43:55+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of kevin009/lamatama
Dataset automatically created during the evaluation run of model kevin009/lamatama on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-13T20:41:45.535254(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of kevin009/lamatama\n\n\n\nDataset automatically created during the evaluation run of model kevin009/lamatama on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T20:41:45.535254(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of kevin009/lamatama\n\n\n\nDataset automatically created during the evaluation run of model kevin009/lamatama on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T20:41:45.535254(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
da169aea4b814f9e072078d7e6091823c8a876d0 |
# Dataset of atlanta/アトランタ/亚特兰大 (Azur Lane)
This is the dataset of atlanta/アトランタ/亚特兰大 (Azur Lane), containing 19 images and their tags.
The core tags of this character are `pink_hair, blue_eyes, braid, long_hair, ahoge, bangs, crown_braid, black_ribbon, hair_ribbon, ribbon, breasts, hair_ornament, hair_between_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 19 | 16.02 MiB | [Download](https://huggingface.co/datasets/CyberHarem/atlanta_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 19 | 11.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/atlanta_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 37 | 20.25 MiB | [Download](https://huggingface.co/datasets/CyberHarem/atlanta_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 19 | 14.88 MiB | [Download](https://huggingface.co/datasets/CyberHarem/atlanta_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 37 | 24.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/atlanta_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/atlanta_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 19 |  |  |  |  |  | 1girl, looking_at_viewer, solo, fingerless_gloves, red_necktie, white_shirt, bare_shoulders, blue_skirt, pleated_skirt, white_thighhighs, blush, simple_background, single_thighhigh, white_background, detached_collar, miniskirt, detached_sleeves, off-shoulder_shirt, open_mouth, smile, red_gloves |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | fingerless_gloves | red_necktie | white_shirt | bare_shoulders | blue_skirt | pleated_skirt | white_thighhighs | blush | simple_background | single_thighhigh | white_background | detached_collar | miniskirt | detached_sleeves | off-shoulder_shirt | open_mouth | smile | red_gloves |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:--------------------|:--------------|:--------------|:-----------------|:-------------|:----------------|:-------------------|:--------|:--------------------|:-------------------|:-------------------|:------------------|:------------|:-------------------|:---------------------|:-------------|:--------|:-------------|
| 0 | 19 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/atlanta_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T20:43:34+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T20:48:11+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of atlanta/アトランタ/亚特兰大 (Azur Lane)
=========================================
This is the dataset of atlanta/アトランタ/亚特兰大 (Azur Lane), containing 19 images and their tags.
The core tags of this character are 'pink\_hair, blue\_eyes, braid, long\_hair, ahoge, bangs, crown\_braid, black\_ribbon, hair\_ribbon, ribbon, breasts, hair\_ornament, hair\_between\_eyes', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
f69fa4ce6b5843be6485888ebb66c9cda9550e0b |
# Dataset of chitose/千歳/千岁 (Azur Lane)
This is the dataset of chitose/千歳/千岁 (Azur Lane), containing 32 images and their tags.
The core tags of this character are `breasts, long_hair, large_breasts, red_hair, purple_eyes, bangs, hat, mask_on_head, sun_hat`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 32 | 56.95 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chitose_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 32 | 31.85 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chitose_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 78 | 62.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chitose_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 32 | 50.88 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chitose_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 78 | 94.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chitose_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/chitose_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 13 |  |  |  |  |  | 1girl, looking_at_viewer, red_bikini, straw_hat, solo, side-tie_bikini_bottom, blush, outdoors, purple_hair |
| 1 | 13 |  |  |  |  |  | 1girl, looking_at_viewer, fox_mask, solo, bare_shoulders, cleavage, blush, japanese_clothes, skirt, wide_sleeves, simple_background, veil, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | red_bikini | straw_hat | solo | side-tie_bikini_bottom | blush | outdoors | purple_hair | fox_mask | bare_shoulders | cleavage | japanese_clothes | skirt | wide_sleeves | simple_background | veil | white_background |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------------|:------------|:-------|:-------------------------|:--------|:-----------|:--------------|:-----------|:-----------------|:-----------|:-------------------|:--------|:---------------|:--------------------|:-------|:-------------------|
| 0 | 13 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | | | | | | | | | |
| 1 | 13 |  |  |  |  |  | X | X | | | X | | X | | | X | X | X | X | X | X | X | X | X |
| CyberHarem/chitose_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T20:43:37+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T20:55:29+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of chitose/千歳/千岁 (Azur Lane)
====================================
This is the dataset of chitose/千歳/千岁 (Azur Lane), containing 32 images and their tags.
The core tags of this character are 'breasts, long\_hair, large\_breasts, red\_hair, purple\_eyes, bangs, hat, mask\_on\_head, sun\_hat', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
a27fd37029bb797ff87139b156d43e67469825cf |
# Dataset Card for Evaluation run of VitalContribution/Evangelion-7B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [VitalContribution/Evangelion-7B](https://huggingface.co/VitalContribution/Evangelion-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_VitalContribution__Evangelion-7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T20:42:12.664551](https://huggingface.co/datasets/open-llm-leaderboard/details_VitalContribution__Evangelion-7B/blob/main/results_2024-01-13T20-42-12.664551.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6436279768533524,
"acc_stderr": 0.03212568059056159,
"acc_norm": 0.6443410769989211,
"acc_norm_stderr": 0.032776722271689644,
"mc1": 0.4773561811505508,
"mc1_stderr": 0.017485542258489646,
"mc2": 0.6400670036423886,
"mc2_stderr": 0.014997645589691178
},
"harness|arc:challenge|25": {
"acc": 0.6544368600682594,
"acc_stderr": 0.013896938461145678,
"acc_norm": 0.689419795221843,
"acc_norm_stderr": 0.013522292098053064
},
"harness|hellaswag|10": {
"acc": 0.6756622186815375,
"acc_stderr": 0.004671701705567242,
"acc_norm": 0.8644692292372037,
"acc_norm_stderr": 0.0034159007223818934
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.29,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.29,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6148148148148148,
"acc_stderr": 0.04203921040156279,
"acc_norm": 0.6148148148148148,
"acc_norm_stderr": 0.04203921040156279
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6973684210526315,
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}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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## Uses
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### Direct Use
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## Dataset Structure
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Annotation process
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#### Personal and Sensitive Information
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_VitalContribution__Evangelion-7B | [
"region:us"
] | 2024-01-13T20:44:31+00:00 | {"pretty_name": "Evaluation run of VitalContribution/Evangelion-7B", "dataset_summary": "Dataset automatically created during the evaluation run of model [VitalContribution/Evangelion-7B](https://huggingface.co/VitalContribution/Evangelion-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_VitalContribution__Evangelion-7B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T20:42:12.664551](https://huggingface.co/datasets/open-llm-leaderboard/details_VitalContribution__Evangelion-7B/blob/main/results_2024-01-13T20-42-12.664551.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6436279768533524,\n \"acc_stderr\": 0.03212568059056159,\n \"acc_norm\": 0.6443410769989211,\n \"acc_norm_stderr\": 0.032776722271689644,\n \"mc1\": 0.4773561811505508,\n \"mc1_stderr\": 0.017485542258489646,\n \"mc2\": 0.6400670036423886,\n \"mc2_stderr\": 0.014997645589691178\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6544368600682594,\n \"acc_stderr\": 0.013896938461145678,\n \"acc_norm\": 0.689419795221843,\n \"acc_norm_stderr\": 0.013522292098053064\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6756622186815375,\n \"acc_stderr\": 0.004671701705567242,\n \"acc_norm\": 0.8644692292372037,\n \"acc_norm_stderr\": 0.0034159007223818934\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6148148148148148,\n \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6973684210526315,\n \"acc_stderr\": 0.03738520676119669,\n \"acc_norm\": 0.6973684210526315,\n \"acc_norm_stderr\": 0.03738520676119669\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.62,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.62,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.7018867924528301,\n \"acc_stderr\": 0.028152837942493857,\n \"acc_norm\": 0.7018867924528301,\n \"acc_norm_stderr\": 0.028152837942493857\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7569444444444444,\n \"acc_stderr\": 0.03586879280080341,\n \"acc_norm\": 0.7569444444444444,\n \"acc_norm_stderr\": 0.03586879280080341\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 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"latest", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T20-42-12.664551.parquet"]}]}, {"config_name": "harness_hendrycksTest_machine_learning_5", "data_files": [{"split": "2024_01_13T20_42_12.664551", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T20-42-12.664551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T20-42-12.664551.parquet"]}]}, {"config_name": "harness_hendrycksTest_management_5", "data_files": [{"split": "2024_01_13T20_42_12.664551", "path": ["**/details_harness|hendrycksTest-management|5_2024-01-13T20-42-12.664551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-management|5_2024-01-13T20-42-12.664551.parquet"]}]}, {"config_name": "harness_hendrycksTest_marketing_5", "data_files": [{"split": "2024_01_13T20_42_12.664551", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-13T20-42-12.664551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-13T20-42-12.664551.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2024_01_13T20_42_12.664551", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T20-42-12.664551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T20-42-12.664551.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2024_01_13T20_42_12.664551", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T20-42-12.664551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T20-42-12.664551.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2024_01_13T20_42_12.664551", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T20-42-12.664551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T20-42-12.664551.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2024_01_13T20_42_12.664551", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T20-42-12.664551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T20-42-12.664551.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2024_01_13T20_42_12.664551", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T20-42-12.664551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T20-42-12.664551.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2024_01_13T20_42_12.664551", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T20-42-12.664551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T20-42-12.664551.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2024_01_13T20_42_12.664551", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T20-42-12.664551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T20-42-12.664551.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2024_01_13T20_42_12.664551", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T20-42-12.664551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T20-42-12.664551.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2024_01_13T20_42_12.664551", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T20-42-12.664551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T20-42-12.664551.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_01_13T20_42_12.664551", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T20-42-12.664551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T20-42-12.664551.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_01_13T20_42_12.664551", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T20-42-12.664551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T20-42-12.664551.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_01_13T20_42_12.664551", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T20-42-12.664551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T20-42-12.664551.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_01_13T20_42_12.664551", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T20-42-12.664551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T20-42-12.664551.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_01_13T20_42_12.664551", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T20-42-12.664551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T20-42-12.664551.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_01_13T20_42_12.664551", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T20-42-12.664551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T20-42-12.664551.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_13T20_42_12.664551", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T20-42-12.664551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T20-42-12.664551.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_13T20_42_12.664551", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T20-42-12.664551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T20-42-12.664551.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_13T20_42_12.664551", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T20-42-12.664551.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T20-42-12.664551.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T20_42_12.664551", "path": ["**/details_harness|winogrande|5_2024-01-13T20-42-12.664551.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T20-42-12.664551.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T20_42_12.664551", "path": ["results_2024-01-13T20-42-12.664551.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T20-42-12.664551.parquet"]}]}]} | 2024-01-13T20:44:53+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of VitalContribution/Evangelion-7B
Dataset automatically created during the evaluation run of model VitalContribution/Evangelion-7B on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-13T20:42:12.664551(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of VitalContribution/Evangelion-7B\n\n\n\nDataset automatically created during the evaluation run of model VitalContribution/Evangelion-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T20:42:12.664551(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of VitalContribution/Evangelion-7B\n\n\n\nDataset automatically created during the evaluation run of model VitalContribution/Evangelion-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T20:42:12.664551(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
62851b1015a6f4dcae2e2b5b6016794f5d6d0e04 |
# Dataset Card for Evaluation run of andysalerno/openchat-nectar-0.3
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [andysalerno/openchat-nectar-0.3](https://huggingface.co/andysalerno/openchat-nectar-0.3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_andysalerno__openchat-nectar-0.3",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T20:54:22.741821](https://huggingface.co/datasets/open-llm-leaderboard/details_andysalerno__openchat-nectar-0.3/blob/main/results_2024-01-13T20-54-22.741821.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6567583142066592,
"acc_stderr": 0.03181491575998619,
"acc_norm": 0.6577099601186088,
"acc_norm_stderr": 0.03246653894337514,
"mc1": 0.3623011015911873,
"mc1_stderr": 0.016826646897262258,
"mc2": 0.5238305779540199,
"mc2_stderr": 0.015355411809850603
},
"harness|arc:challenge|25": {
"acc": 0.621160409556314,
"acc_stderr": 0.014175915490000326,
"acc_norm": 0.659556313993174,
"acc_norm_stderr": 0.013847460518892976
},
"harness|hellaswag|10": {
"acc": 0.6349332802230632,
"acc_stderr": 0.004804649197163696,
"acc_norm": 0.8315076677952599,
"acc_norm_stderr": 0.0037353793752550124
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.35,
"acc_stderr": 0.0479372485441102,
"acc_norm": 0.35,
"acc_norm_stderr": 0.0479372485441102
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6296296296296297,
"acc_stderr": 0.041716541613545426,
"acc_norm": 0.6296296296296297,
"acc_norm_stderr": 0.041716541613545426
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6973684210526315,
"acc_stderr": 0.037385206761196686,
"acc_norm": 0.6973684210526315,
"acc_norm_stderr": 0.037385206761196686
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.66,
"acc_stderr": 0.04760952285695237,
"acc_norm": 0.66,
"acc_norm_stderr": 0.04760952285695237
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7132075471698113,
"acc_stderr": 0.027834912527544067,
"acc_norm": 0.7132075471698113,
"acc_norm_stderr": 0.027834912527544067
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.03476590104304134,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.03476590104304134
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.49,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.49,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.59,
"acc_stderr": 0.04943110704237102,
"acc_norm": 0.59,
"acc_norm_stderr": 0.04943110704237102
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.39,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.39,
"acc_norm_stderr": 0.04902071300001975
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6878612716763006,
"acc_stderr": 0.03533133389323657,
"acc_norm": 0.6878612716763006,
"acc_norm_stderr": 0.03533133389323657
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4019607843137255,
"acc_stderr": 0.048786087144669955,
"acc_norm": 0.4019607843137255,
"acc_norm_stderr": 0.048786087144669955
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.74,
"acc_stderr": 0.04408440022768079,
"acc_norm": 0.74,
"acc_norm_stderr": 0.04408440022768079
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.6042553191489362,
"acc_stderr": 0.031967586978353627,
"acc_norm": 0.6042553191489362,
"acc_norm_stderr": 0.031967586978353627
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.49122807017543857,
"acc_stderr": 0.047028804320496165,
"acc_norm": 0.49122807017543857,
"acc_norm_stderr": 0.047028804320496165
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.593103448275862,
"acc_stderr": 0.04093793981266236,
"acc_norm": 0.593103448275862,
"acc_norm_stderr": 0.04093793981266236
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.42063492063492064,
"acc_stderr": 0.025424835086924006,
"acc_norm": 0.42063492063492064,
"acc_norm_stderr": 0.025424835086924006
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.5079365079365079,
"acc_stderr": 0.044715725362943486,
"acc_norm": 0.5079365079365079,
"acc_norm_stderr": 0.044715725362943486
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.29,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.29,
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}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## Dataset Structure
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Annotation process
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#### Personal and Sensitive Information
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_andysalerno__openchat-nectar-0.3 | [
"region:us"
] | 2024-01-13T20:56:41+00:00 | {"pretty_name": "Evaluation run of andysalerno/openchat-nectar-0.3", "dataset_summary": "Dataset automatically created during the evaluation run of model [andysalerno/openchat-nectar-0.3](https://huggingface.co/andysalerno/openchat-nectar-0.3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_andysalerno__openchat-nectar-0.3\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T20:54:22.741821](https://huggingface.co/datasets/open-llm-leaderboard/details_andysalerno__openchat-nectar-0.3/blob/main/results_2024-01-13T20-54-22.741821.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6567583142066592,\n \"acc_stderr\": 0.03181491575998619,\n \"acc_norm\": 0.6577099601186088,\n \"acc_norm_stderr\": 0.03246653894337514,\n \"mc1\": 0.3623011015911873,\n \"mc1_stderr\": 0.016826646897262258,\n \"mc2\": 0.5238305779540199,\n \"mc2_stderr\": 0.015355411809850603\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.621160409556314,\n \"acc_stderr\": 0.014175915490000326,\n \"acc_norm\": 0.659556313993174,\n \"acc_norm_stderr\": 0.013847460518892976\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6349332802230632,\n \"acc_stderr\": 0.004804649197163696,\n \"acc_norm\": 0.8315076677952599,\n \"acc_norm_stderr\": 0.0037353793752550124\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6296296296296297,\n \"acc_stderr\": 0.041716541613545426,\n \"acc_norm\": 0.6296296296296297,\n \"acc_norm_stderr\": 0.041716541613545426\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6973684210526315,\n \"acc_stderr\": 0.037385206761196686,\n \"acc_norm\": 0.6973684210526315,\n \"acc_norm_stderr\": 0.037385206761196686\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.7132075471698113,\n \"acc_stderr\": 0.027834912527544067,\n \"acc_norm\": 0.7132075471698113,\n \"acc_norm_stderr\": 0.027834912527544067\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.59,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6878612716763006,\n \"acc_stderr\": 0.03533133389323657,\n \"acc_norm\": 0.6878612716763006,\n \"acc_norm_stderr\": 0.03533133389323657\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.048786087144669955,\n \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.048786087144669955\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.6042553191489362,\n \"acc_stderr\": 0.031967586978353627,\n \"acc_norm\": 0.6042553191489362,\n \"acc_norm_stderr\": 0.031967586978353627\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.49122807017543857,\n \"acc_stderr\": 0.047028804320496165,\n \"acc_norm\": 0.49122807017543857,\n \"acc_norm_stderr\": 0.047028804320496165\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.593103448275862,\n \"acc_stderr\": 0.04093793981266236,\n \"acc_norm\": 0.593103448275862,\n \"acc_norm_stderr\": 0.04093793981266236\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.42063492063492064,\n \"acc_stderr\": 0.025424835086924006,\n \"acc_norm\": 0.42063492063492064,\n \"acc_norm_stderr\": 0.025424835086924006\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5079365079365079,\n \"acc_stderr\": 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["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T20-54-22.741821.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2024_01_13T20_54_22.741821", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T20-54-22.741821.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T20-54-22.741821.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2024_01_13T20_54_22.741821", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T20-54-22.741821.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T20-54-22.741821.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2024_01_13T20_54_22.741821", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T20-54-22.741821.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T20-54-22.741821.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_01_13T20_54_22.741821", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T20-54-22.741821.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T20-54-22.741821.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_01_13T20_54_22.741821", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T20-54-22.741821.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T20-54-22.741821.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_01_13T20_54_22.741821", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T20-54-22.741821.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T20-54-22.741821.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_01_13T20_54_22.741821", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T20-54-22.741821.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T20-54-22.741821.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_01_13T20_54_22.741821", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T20-54-22.741821.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T20-54-22.741821.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_01_13T20_54_22.741821", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T20-54-22.741821.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T20-54-22.741821.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_13T20_54_22.741821", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T20-54-22.741821.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T20-54-22.741821.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_13T20_54_22.741821", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T20-54-22.741821.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T20-54-22.741821.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_13T20_54_22.741821", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T20-54-22.741821.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T20-54-22.741821.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T20_54_22.741821", "path": ["**/details_harness|winogrande|5_2024-01-13T20-54-22.741821.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T20-54-22.741821.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T20_54_22.741821", "path": ["results_2024-01-13T20-54-22.741821.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T20-54-22.741821.parquet"]}]}]} | 2024-01-13T20:57:01+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of andysalerno/openchat-nectar-0.3
Dataset automatically created during the evaluation run of model andysalerno/openchat-nectar-0.3 on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-13T20:54:22.741821(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of andysalerno/openchat-nectar-0.3\n\n\n\nDataset automatically created during the evaluation run of model andysalerno/openchat-nectar-0.3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T20:54:22.741821(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of andysalerno/openchat-nectar-0.3\n\n\n\nDataset automatically created during the evaluation run of model andysalerno/openchat-nectar-0.3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T20:54:22.741821(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
a3b1592cec488197d020882632e408b35f928556 |
# Dataset Card for Evaluation run of TomGrc/FusionNet_7Bx2_MoE_14B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [TomGrc/FusionNet_7Bx2_MoE_14B](https://huggingface.co/TomGrc/FusionNet_7Bx2_MoE_14B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_TomGrc__FusionNet_7Bx2_MoE_14B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T20:54:40.913978](https://huggingface.co/datasets/open-llm-leaderboard/details_TomGrc__FusionNet_7Bx2_MoE_14B/blob/main/results_2024-01-13T20-54-40.913978.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6538972683722968,
"acc_stderr": 0.0320392240520873,
"acc_norm": 0.6524071431642017,
"acc_norm_stderr": 0.032729125210872866,
"mc1": 0.5716034271725826,
"mc1_stderr": 0.017323088597314743,
"mc2": 0.6959760844818743,
"mc2_stderr": 0.01511859520186482
},
"harness|arc:challenge|25": {
"acc": 0.7039249146757679,
"acc_stderr": 0.013340916085246258,
"acc_norm": 0.735494880546075,
"acc_norm_stderr": 0.012889272949313368
},
"harness|hellaswag|10": {
"acc": 0.7274447321250747,
"acc_stderr": 0.004443639394177423,
"acc_norm": 0.8883688508265286,
"acc_norm_stderr": 0.0031426851645672675
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252605,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252605
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6592592592592592,
"acc_stderr": 0.04094376269996792,
"acc_norm": 0.6592592592592592,
"acc_norm_stderr": 0.04094376269996792
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6907894736842105,
"acc_stderr": 0.037610708698674805,
"acc_norm": 0.6907894736842105,
"acc_norm_stderr": 0.037610708698674805
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.65,
"acc_stderr": 0.0479372485441102,
"acc_norm": 0.65,
"acc_norm_stderr": 0.0479372485441102
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.720754716981132,
"acc_stderr": 0.027611163402399715,
"acc_norm": 0.720754716981132,
"acc_norm_stderr": 0.027611163402399715
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7708333333333334,
"acc_stderr": 0.03514697467862388,
"acc_norm": 0.7708333333333334,
"acc_norm_stderr": 0.03514697467862388
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.47,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.47,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.53,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.53,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6647398843930635,
"acc_stderr": 0.03599586301247077,
"acc_norm": 0.6647398843930635,
"acc_norm_stderr": 0.03599586301247077
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4411764705882353,
"acc_stderr": 0.049406356306056595,
"acc_norm": 0.4411764705882353,
"acc_norm_stderr": 0.049406356306056595
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5957446808510638,
"acc_stderr": 0.03208115750788684,
"acc_norm": 0.5957446808510638,
"acc_norm_stderr": 0.03208115750788684
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.47368421052631576,
"acc_stderr": 0.046970851366478626,
"acc_norm": 0.47368421052631576,
"acc_norm_stderr": 0.046970851366478626
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5793103448275863,
"acc_stderr": 0.0411391498118926,
"acc_norm": 0.5793103448275863,
"acc_norm_stderr": 0.0411391498118926
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4365079365079365,
"acc_stderr": 0.0255428468174005,
"acc_norm": 0.4365079365079365,
"acc_norm_stderr": 0.0255428468174005
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.42063492063492064,
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"acc_norm": 0.42063492063492064,
"acc_norm_stderr": 0.04415438226743744
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.33,
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"acc_norm": 0.33,
"acc_norm_stderr": 0.047258156262526045
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7741935483870968,
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"acc_norm": 0.7741935483870968,
"acc_norm_stderr": 0.023785577884181012
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5073891625615764,
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"acc_norm": 0.5073891625615764,
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},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.69,
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"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7757575757575758,
"acc_stderr": 0.03256866661681102,
"acc_norm": 0.7757575757575758,
"acc_norm_stderr": 0.03256866661681102
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7828282828282829,
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"acc_norm": 0.7828282828282829,
"acc_norm_stderr": 0.029376616484945633
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.917098445595855,
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},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6666666666666666,
"acc_stderr": 0.023901157979402538,
"acc_norm": 0.6666666666666666,
"acc_norm_stderr": 0.023901157979402538
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.35185185185185186,
"acc_stderr": 0.029116617606083008,
"acc_norm": 0.35185185185185186,
"acc_norm_stderr": 0.029116617606083008
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6764705882352942,
"acc_stderr": 0.03038835355188679,
"acc_norm": 0.6764705882352942,
"acc_norm_stderr": 0.03038835355188679
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.33774834437086093,
"acc_stderr": 0.03861557546255169,
"acc_norm": 0.33774834437086093,
"acc_norm_stderr": 0.03861557546255169
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8422018348623853,
"acc_stderr": 0.015630022970092444,
"acc_norm": 0.8422018348623853,
"acc_norm_stderr": 0.015630022970092444
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.49537037037037035,
"acc_stderr": 0.03409825519163572,
"acc_norm": 0.49537037037037035,
"acc_norm_stderr": 0.03409825519163572
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8431372549019608,
"acc_stderr": 0.02552472232455335,
"acc_norm": 0.8431372549019608,
"acc_norm_stderr": 0.02552472232455335
},
"harness|hendrycksTest-high_school_world_history|5": {
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"harness|hendrycksTest-jurisprudence|5": {
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"harness|hendrycksTest-logical_fallacies|5": {
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"harness|hendrycksTest-miscellaneous|5": {
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"harness|hendrycksTest-moral_disputes|5": {
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"acc_norm_stderr": 0.023703099525258176
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"harness|hendrycksTest-moral_scenarios|5": {
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"harness|hendrycksTest-nutrition|5": {
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"harness|hendrycksTest-philosophy|5": {
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"harness|hendrycksTest-professional_accounting|5": {
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"harness|hendrycksTest-public_relations|5": {
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"harness|hendrycksTest-security_studies|5": {
"acc": 0.726530612244898,
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"acc_norm": 0.726530612244898,
"acc_norm_stderr": 0.028535560337128448
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"harness|hendrycksTest-sociology|5": {
"acc": 0.8507462686567164,
"acc_stderr": 0.02519692987482705,
"acc_norm": 0.8507462686567164,
"acc_norm_stderr": 0.02519692987482705
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"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.84,
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"acc_norm": 0.84,
"acc_norm_stderr": 0.03684529491774708
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"harness|hendrycksTest-virology|5": {
"acc": 0.5481927710843374,
"acc_stderr": 0.03874371556587953,
"acc_norm": 0.5481927710843374,
"acc_norm_stderr": 0.03874371556587953
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"harness|hendrycksTest-world_religions|5": {
"acc": 0.8304093567251462,
"acc_stderr": 0.02878210810540171,
"acc_norm": 0.8304093567251462,
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"harness|truthfulqa:mc|0": {
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"mc1_stderr": 0.017323088597314743,
"mc2": 0.6959760844818743,
"mc2_stderr": 0.01511859520186482
},
"harness|winogrande|5": {
"acc": 0.8816101026045777,
"acc_stderr": 0.009079851554821855
},
"harness|gsm8k|5": {
"acc": 0.7065959059893859,
"acc_stderr": 0.012541830815461492
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## Dataset Structure
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Annotation process
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#### Personal and Sensitive Information
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## Bias, Risks, and Limitations
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[More Information Needed] | open-llm-leaderboard/details_TomGrc__FusionNet_7Bx2_MoE_14B | [
"region:us"
] | 2024-01-13T20:56:55+00:00 | {"pretty_name": "Evaluation run of TomGrc/FusionNet_7Bx2_MoE_14B", "dataset_summary": "Dataset automatically created during the evaluation run of model [TomGrc/FusionNet_7Bx2_MoE_14B](https://huggingface.co/TomGrc/FusionNet_7Bx2_MoE_14B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_TomGrc__FusionNet_7Bx2_MoE_14B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T20:54:40.913978](https://huggingface.co/datasets/open-llm-leaderboard/details_TomGrc__FusionNet_7Bx2_MoE_14B/blob/main/results_2024-01-13T20-54-40.913978.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6538972683722968,\n \"acc_stderr\": 0.0320392240520873,\n \"acc_norm\": 0.6524071431642017,\n \"acc_norm_stderr\": 0.032729125210872866,\n \"mc1\": 0.5716034271725826,\n \"mc1_stderr\": 0.017323088597314743,\n \"mc2\": 0.6959760844818743,\n \"mc2_stderr\": 0.01511859520186482\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.7039249146757679,\n \"acc_stderr\": 0.013340916085246258,\n \"acc_norm\": 0.735494880546075,\n \"acc_norm_stderr\": 0.012889272949313368\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7274447321250747,\n \"acc_stderr\": 0.004443639394177423,\n \"acc_norm\": 0.8883688508265286,\n \"acc_norm_stderr\": 0.0031426851645672675\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6592592592592592,\n \"acc_stderr\": 0.04094376269996792,\n \"acc_norm\": 0.6592592592592592,\n \"acc_norm_stderr\": 0.04094376269996792\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6907894736842105,\n \"acc_stderr\": 0.037610708698674805,\n \"acc_norm\": 0.6907894736842105,\n \"acc_norm_stderr\": 0.037610708698674805\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.65,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.720754716981132,\n \"acc_stderr\": 0.027611163402399715,\n \"acc_norm\": 0.720754716981132,\n \"acc_norm_stderr\": 0.027611163402399715\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7708333333333334,\n \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n \"acc_norm_stderr\": 0.03514697467862388\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6647398843930635,\n \"acc_stderr\": 0.03599586301247077,\n \"acc_norm\": 0.6647398843930635,\n \"acc_norm_stderr\": 0.03599586301247077\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.049406356306056595,\n \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.049406356306056595\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5957446808510638,\n \"acc_stderr\": 0.03208115750788684,\n \"acc_norm\": 0.5957446808510638,\n \"acc_norm_stderr\": 0.03208115750788684\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.47368421052631576,\n \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.47368421052631576,\n \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5793103448275863,\n \"acc_stderr\": 0.0411391498118926,\n \"acc_norm\": 0.5793103448275863,\n \"acc_norm_stderr\": 0.0411391498118926\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.4365079365079365,\n \"acc_stderr\": 0.0255428468174005,\n \"acc_norm\": 0.4365079365079365,\n \"acc_norm_stderr\": 0.0255428468174005\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42063492063492064,\n \"acc_stderr\": 0.04415438226743744,\n \"acc_norm\": 0.42063492063492064,\n \"acc_norm_stderr\": 0.04415438226743744\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7741935483870968,\n \"acc_stderr\": 0.023785577884181012,\n \"acc_norm\": 0.7741935483870968,\n \"acc_norm_stderr\": 0.023785577884181012\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5073891625615764,\n \"acc_stderr\": 0.035176035403610105,\n \"acc_norm\": 0.5073891625615764,\n \"acc_norm_stderr\": 0.035176035403610105\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.03256866661681102,\n \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.03256866661681102\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7828282828282829,\n \"acc_stderr\": 0.029376616484945633,\n \"acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.029376616484945633\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.917098445595855,\n \"acc_stderr\": 0.01989934131572178,\n \"acc_norm\": 0.917098445595855,\n \"acc_norm_stderr\": 0.01989934131572178\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.023901157979402538,\n \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.023901157979402538\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.35185185185185186,\n \"acc_stderr\": 0.029116617606083008,\n \"acc_norm\": 0.35185185185185186,\n \"acc_norm_stderr\": 0.029116617606083008\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.03038835355188679,\n \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.03038835355188679\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.33774834437086093,\n \"acc_stderr\": 0.03861557546255169,\n \"acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.03861557546255169\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8422018348623853,\n \"acc_stderr\": 0.015630022970092444,\n \"acc_norm\": 0.8422018348623853,\n \"acc_norm_stderr\": 0.015630022970092444\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.49537037037037035,\n \"acc_stderr\": 0.03409825519163572,\n \"acc_norm\": 0.49537037037037035,\n \"acc_norm_stderr\": 0.03409825519163572\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8431372549019608,\n \"acc_stderr\": 0.02552472232455335,\n \"acc_norm\": 0.8431372549019608,\n \"acc_norm_stderr\": 0.02552472232455335\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7974683544303798,\n \"acc_stderr\": 0.026160568246601443,\n \"acc_norm\": 0.7974683544303798,\n \"acc_norm_stderr\": 0.026160568246601443\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n \"acc_stderr\": 0.031024411740572213,\n \"acc_norm\": 0.6905829596412556,\n \"acc_norm_stderr\": 0.031024411740572213\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.034981493854624714,\n \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.034981493854624714\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 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["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T20-54-40.913978.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T20-54-40.913978.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_13T20_54_40.913978", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T20-54-40.913978.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T20-54-40.913978.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_13T20_54_40.913978", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T20-54-40.913978.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T20-54-40.913978.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_13T20_54_40.913978", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T20-54-40.913978.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T20-54-40.913978.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T20_54_40.913978", "path": ["**/details_harness|winogrande|5_2024-01-13T20-54-40.913978.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T20-54-40.913978.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T20_54_40.913978", "path": ["results_2024-01-13T20-54-40.913978.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T20-54-40.913978.parquet"]}]}]} | 2024-01-13T20:57:16+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of TomGrc/FusionNet_7Bx2_MoE_14B
Dataset automatically created during the evaluation run of model TomGrc/FusionNet_7Bx2_MoE_14B on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-13T20:54:40.913978(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of TomGrc/FusionNet_7Bx2_MoE_14B\n\n\n\nDataset automatically created during the evaluation run of model TomGrc/FusionNet_7Bx2_MoE_14B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T20:54:40.913978(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of TomGrc/FusionNet_7Bx2_MoE_14B\n\n\n\nDataset automatically created during the evaluation run of model TomGrc/FusionNet_7Bx2_MoE_14B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T20:54:40.913978(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
90489b1aed11259c248c06524dae62a199eab09a |
# Dataset Card for Evaluation run of SicariusSicariiStuff/Tenebra_30B_Alpha01_FP16
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [SicariusSicariiStuff/Tenebra_30B_Alpha01_FP16](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_FP16) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_SicariusSicariiStuff__Tenebra_30B_Alpha01_FP16",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T21:09:42.691058](https://huggingface.co/datasets/open-llm-leaderboard/details_SicariusSicariiStuff__Tenebra_30B_Alpha01_FP16/blob/main/results_2024-01-13T21-09-42.691058.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
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"acc_stderr": 0.033739092927066276,
"acc_norm": 0.5497582392701649,
"acc_norm_stderr": 0.03446388375818918,
"mc1": 0.37209302325581395,
"mc1_stderr": 0.016921090118814035,
"mc2": 0.542164417620334,
"mc2_stderr": 0.015177868903320643
},
"harness|arc:challenge|25": {
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"acc_stderr": 0.014124597881844461,
"acc_norm": 0.6450511945392492,
"acc_norm_stderr": 0.013983036904094097
},
"harness|hellaswag|10": {
"acc": 0.6488747261501693,
"acc_stderr": 0.004763465139038567,
"acc_norm": 0.8479386576379208,
"acc_norm_stderr": 0.0035834648107534763
},
"harness|hendrycksTest-abstract_algebra|5": {
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"acc_stderr": 0.04793724854411021,
"acc_norm": 0.35,
"acc_norm_stderr": 0.04793724854411021
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5555555555555556,
"acc_stderr": 0.04292596718256981,
"acc_norm": 0.5555555555555556,
"acc_norm_stderr": 0.04292596718256981
},
"harness|hendrycksTest-astronomy|5": {
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"acc_stderr": 0.039993097127774734,
"acc_norm": 0.5921052631578947,
"acc_norm_stderr": 0.039993097127774734
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.53,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.53,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-clinical_knowledge|5": {
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"acc_stderr": 0.03065674869673943,
"acc_norm": 0.5433962264150943,
"acc_norm_stderr": 0.03065674869673943
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6111111111111112,
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"acc_norm": 0.6111111111111112,
"acc_norm_stderr": 0.04076663253918567
},
"harness|hendrycksTest-college_chemistry|5": {
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"acc_norm": 0.43,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.5,
"acc_stderr": 0.050251890762960605,
"acc_norm": 0.5,
"acc_norm_stderr": 0.050251890762960605
},
"harness|hendrycksTest-college_mathematics|5": {
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},
"harness|hendrycksTest-college_medicine|5": {
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},
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},
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},
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},
"harness|hendrycksTest-econometrics|5": {
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"acc_norm": 0.39473684210526316,
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},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.42758620689655175,
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"acc_norm": 0.42758620689655175,
"acc_norm_stderr": 0.04122737111370332
},
"harness|hendrycksTest-elementary_mathematics|5": {
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"harness|hendrycksTest-formal_logic|5": {
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"harness|hendrycksTest-global_facts|5": {
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"harness|hendrycksTest-high_school_biology|5": {
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"harness|hendrycksTest-high_school_chemistry|5": {
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"harness|hendrycksTest-high_school_computer_science|5": {
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"harness|hendrycksTest-high_school_european_history|5": {
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},
"harness|hendrycksTest-high_school_geography|5": {
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},
"harness|hendrycksTest-high_school_macroeconomics|5": {
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},
"harness|hendrycksTest-high_school_mathematics|5": {
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},
"harness|hendrycksTest-high_school_microeconomics|5": {
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},
"harness|hendrycksTest-high_school_physics|5": {
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"harness|hendrycksTest-high_school_psychology|5": {
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},
"harness|hendrycksTest-high_school_statistics|5": {
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"harness|hendrycksTest-high_school_us_history|5": {
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},
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},
"harness|hendrycksTest-moral_disputes|5": {
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},
"harness|hendrycksTest-moral_scenarios|5": {
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},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6816720257234726,
"acc_stderr": 0.026457225067811032,
"acc_norm": 0.6816720257234726,
"acc_norm_stderr": 0.026457225067811032
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.6388888888888888,
"acc_stderr": 0.026725868809100793,
"acc_norm": 0.6388888888888888,
"acc_norm_stderr": 0.026725868809100793
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.40070921985815605,
"acc_stderr": 0.029233465745573086,
"acc_norm": 0.40070921985815605,
"acc_norm_stderr": 0.029233465745573086
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.39765319426336376,
"acc_stderr": 0.012499840347460645,
"acc_norm": 0.39765319426336376,
"acc_norm_stderr": 0.012499840347460645
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.5882352941176471,
"acc_stderr": 0.02989616303312547,
"acc_norm": 0.5882352941176471,
"acc_norm_stderr": 0.02989616303312547
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.5816993464052288,
"acc_stderr": 0.019955975145835546,
"acc_norm": 0.5816993464052288,
"acc_norm_stderr": 0.019955975145835546
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6090909090909091,
"acc_stderr": 0.0467375233367024,
"acc_norm": 0.6090909090909091,
"acc_norm_stderr": 0.0467375233367024
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.6,
"acc_stderr": 0.031362502409358936,
"acc_norm": 0.6,
"acc_norm_stderr": 0.031362502409358936
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.7114427860696517,
"acc_stderr": 0.03203841040213322,
"acc_norm": 0.7114427860696517,
"acc_norm_stderr": 0.03203841040213322
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.83,
"acc_stderr": 0.0377525168068637,
"acc_norm": 0.83,
"acc_norm_stderr": 0.0377525168068637
},
"harness|hendrycksTest-virology|5": {
"acc": 0.46987951807228917,
"acc_stderr": 0.03885425420866767,
"acc_norm": 0.46987951807228917,
"acc_norm_stderr": 0.03885425420866767
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.7251461988304093,
"acc_stderr": 0.034240429246915824,
"acc_norm": 0.7251461988304093,
"acc_norm_stderr": 0.034240429246915824
},
"harness|truthfulqa:mc|0": {
"mc1": 0.37209302325581395,
"mc1_stderr": 0.016921090118814035,
"mc2": 0.542164417620334,
"mc2_stderr": 0.015177868903320643
},
"harness|winogrande|5": {
"acc": 0.7861089187056038,
"acc_stderr": 0.01152446695409025
},
"harness|gsm8k|5": {
"acc": 0.24639878695981804,
"acc_stderr": 0.011869498557755346
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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## Uses
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### Out-of-Scope Use
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## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Annotation process
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#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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## Bias, Risks, and Limitations
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_SicariusSicariiStuff__Tenebra_30B_Alpha01_FP16 | [
"region:us"
] | 2024-01-13T21:12:00+00:00 | {"pretty_name": "Evaluation run of SicariusSicariiStuff/Tenebra_30B_Alpha01_FP16", "dataset_summary": "Dataset automatically created during the evaluation run of model [SicariusSicariiStuff/Tenebra_30B_Alpha01_FP16](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_FP16) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_SicariusSicariiStuff__Tenebra_30B_Alpha01_FP16\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T21:09:42.691058](https://huggingface.co/datasets/open-llm-leaderboard/details_SicariusSicariiStuff__Tenebra_30B_Alpha01_FP16/blob/main/results_2024-01-13T21-09-42.691058.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.5451166400770657,\n \"acc_stderr\": 0.033739092927066276,\n \"acc_norm\": 0.5497582392701649,\n \"acc_norm_stderr\": 0.03446388375818918,\n \"mc1\": 0.37209302325581395,\n \"mc1_stderr\": 0.016921090118814035,\n \"mc2\": 0.542164417620334,\n \"mc2_stderr\": 0.015177868903320643\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6279863481228669,\n \"acc_stderr\": 0.014124597881844461,\n \"acc_norm\": 0.6450511945392492,\n \"acc_norm_stderr\": 0.013983036904094097\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6488747261501693,\n \"acc_stderr\": 0.004763465139038567,\n \"acc_norm\": 0.8479386576379208,\n \"acc_norm_stderr\": 0.0035834648107534763\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.04793724854411021,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.04793724854411021\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5555555555555556,\n \"acc_stderr\": 0.04292596718256981,\n \"acc_norm\": 0.5555555555555556,\n \"acc_norm_stderr\": 0.04292596718256981\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.5921052631578947,\n \"acc_stderr\": 0.039993097127774734,\n \"acc_norm\": 0.5921052631578947,\n \"acc_norm_stderr\": 0.039993097127774734\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.5433962264150943,\n \"acc_stderr\": 0.03065674869673943,\n \"acc_norm\": 0.5433962264150943,\n \"acc_norm_stderr\": 0.03065674869673943\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6111111111111112,\n \"acc_stderr\": 0.04076663253918567,\n \"acc_norm\": 0.6111111111111112,\n \"acc_norm_stderr\": 0.04076663253918567\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.4624277456647399,\n \"acc_stderr\": 0.0380168510452446,\n \"acc_norm\": 0.4624277456647399,\n \"acc_norm_stderr\": 0.0380168510452446\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.3431372549019608,\n \"acc_stderr\": 0.04724007352383888,\n \"acc_norm\": 0.3431372549019608,\n \"acc_norm_stderr\": 0.04724007352383888\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.4978723404255319,\n \"acc_stderr\": 0.032685726586674915,\n \"acc_norm\": 0.4978723404255319,\n \"acc_norm_stderr\": 0.032685726586674915\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.39473684210526316,\n \"acc_stderr\": 0.045981880578165414,\n \"acc_norm\": 0.39473684210526316,\n \"acc_norm_stderr\": 0.045981880578165414\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.42758620689655175,\n \"acc_stderr\": 0.04122737111370332,\n \"acc_norm\": 0.42758620689655175,\n \"acc_norm_stderr\": 0.04122737111370332\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.3783068783068783,\n \"acc_stderr\": 0.02497695405315524,\n \"acc_norm\": 0.3783068783068783,\n \"acc_norm_stderr\": 0.02497695405315524\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.36507936507936506,\n \"acc_stderr\": 0.04306241259127153,\n \"acc_norm\": 0.36507936507936506,\n \"acc_norm_stderr\": 0.04306241259127153\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695236,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6774193548387096,\n \"acc_stderr\": 0.02659308451657228,\n \"acc_norm\": 0.6774193548387096,\n \"acc_norm_stderr\": 0.02659308451657228\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.4236453201970443,\n \"acc_stderr\": 0.03476725747649037,\n \"acc_norm\": 0.4236453201970443,\n \"acc_norm_stderr\": 0.03476725747649037\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03225078108306289,\n \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03225078108306289\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7676767676767676,\n \"acc_stderr\": 0.03008862949021749,\n \"acc_norm\": 0.7676767676767676,\n \"acc_norm_stderr\": 0.03008862949021749\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.7875647668393783,\n \"acc_stderr\": 0.029519282616817223,\n \"acc_norm\": 0.7875647668393783,\n \"acc_norm_stderr\": 0.029519282616817223\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.5538461538461539,\n \"acc_stderr\": 0.025203571773028333,\n \"acc_norm\": 0.5538461538461539,\n \"acc_norm_stderr\": 0.025203571773028333\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.24444444444444444,\n \"acc_stderr\": 0.02620276653465215,\n \"acc_norm\": 0.24444444444444444,\n \"acc_norm_stderr\": 0.02620276653465215\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.592436974789916,\n \"acc_stderr\": 0.03191863374478466,\n \"acc_norm\": 0.592436974789916,\n \"acc_norm_stderr\": 0.03191863374478466\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.2980132450331126,\n \"acc_stderr\": 0.037345356767871984,\n \"acc_norm\": 0.2980132450331126,\n \"acc_norm_stderr\": 0.037345356767871984\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.7339449541284404,\n \"acc_stderr\": 0.018946022322225607,\n \"acc_norm\": 0.7339449541284404,\n \"acc_norm_stderr\": 0.018946022322225607\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.4675925925925926,\n \"acc_stderr\": 0.034028015813589656,\n \"acc_norm\": 0.4675925925925926,\n \"acc_norm_stderr\": 0.034028015813589656\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.25980392156862747,\n \"acc_stderr\": 0.03077855467869326,\n \"acc_norm\": 0.25980392156862747,\n \"acc_norm_stderr\": 0.03077855467869326\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7805907172995781,\n \"acc_stderr\": 0.026939106581553945,\n \"acc_norm\": 0.7805907172995781,\n \"acc_norm_stderr\": 0.026939106581553945\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.600896860986547,\n \"acc_stderr\": 0.032867453125679603,\n \"acc_norm\": 0.600896860986547,\n \"acc_norm_stderr\": 0.032867453125679603\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.6335877862595419,\n \"acc_stderr\": 0.04225875451969638,\n \"acc_norm\": 0.6335877862595419,\n \"acc_norm_stderr\": 0.04225875451969638\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.743801652892562,\n \"acc_stderr\": 0.03984979653302871,\n \"acc_norm\": 0.743801652892562,\n \"acc_norm_stderr\": 0.03984979653302871\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6574074074074074,\n \"acc_stderr\": 0.045879047413018105,\n \"acc_norm\": 0.6574074074074074,\n \"acc_norm_stderr\": 0.045879047413018105\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.6809815950920245,\n \"acc_stderr\": 0.03661997551073836,\n \"acc_norm\": 0.6809815950920245,\n \"acc_norm_stderr\": 0.03661997551073836\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.375,\n \"acc_stderr\": 0.04595091388086298,\n \"acc_norm\": 0.375,\n \"acc_norm_stderr\": 0.04595091388086298\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7281553398058253,\n \"acc_stderr\": 0.044052680241409216,\n \"acc_norm\": 0.7281553398058253,\n \"acc_norm_stderr\": 0.044052680241409216\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7991452991452992,\n \"acc_stderr\": 0.026246772946890477,\n \"acc_norm\": 0.7991452991452992,\n \"acc_norm_stderr\": 0.026246772946890477\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.63,\n \"acc_stderr\": 0.04852365870939098,\n \"acc_norm\": 0.63,\n \"acc_norm_stderr\": 0.04852365870939098\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7381864623243933,\n \"acc_stderr\": 0.01572083867844526,\n \"acc_norm\": 0.7381864623243933,\n \"acc_norm_stderr\": 0.01572083867844526\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.630057803468208,\n \"acc_stderr\": 0.02599247202930638,\n \"acc_norm\": 0.630057803468208,\n \"acc_norm_stderr\": 0.02599247202930638\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2927374301675978,\n \"acc_stderr\": 0.015218109544410177,\n \"acc_norm\": 0.2927374301675978,\n \"acc_norm_stderr\": 0.015218109544410177\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.5718954248366013,\n \"acc_stderr\": 0.028332397483664278,\n \"acc_norm\": 0.5718954248366013,\n \"acc_norm_stderr\": 0.028332397483664278\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6816720257234726,\n \"acc_stderr\": 0.026457225067811032,\n \"acc_norm\": 0.6816720257234726,\n \"acc_norm_stderr\": 0.026457225067811032\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.6388888888888888,\n \"acc_stderr\": 0.026725868809100793,\n \"acc_norm\": 0.6388888888888888,\n \"acc_norm_stderr\": 0.026725868809100793\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.40070921985815605,\n \"acc_stderr\": 0.029233465745573086,\n \"acc_norm\": 0.40070921985815605,\n \"acc_norm_stderr\": 0.029233465745573086\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.39765319426336376,\n \"acc_stderr\": 0.012499840347460645,\n \"acc_norm\": 0.39765319426336376,\n \"acc_norm_stderr\": 0.012499840347460645\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.5882352941176471,\n \"acc_stderr\": 0.02989616303312547,\n \"acc_norm\": 0.5882352941176471,\n \"acc_norm_stderr\": 0.02989616303312547\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 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#region-us
|
# Dataset Card for Evaluation run of SicariusSicariiStuff/Tenebra_30B_Alpha01_FP16
Dataset automatically created during the evaluation run of model SicariusSicariiStuff/Tenebra_30B_Alpha01_FP16 on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-13T21:09:42.691058(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
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## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of SicariusSicariiStuff/Tenebra_30B_Alpha01_FP16\n\n\n\nDataset automatically created during the evaluation run of model SicariusSicariiStuff/Tenebra_30B_Alpha01_FP16 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T21:09:42.691058(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of SicariusSicariiStuff/Tenebra_30B_Alpha01_FP16\n\n\n\nDataset automatically created during the evaluation run of model SicariusSicariiStuff/Tenebra_30B_Alpha01_FP16 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T21:09:42.691058(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
1732d912289a0ccdc713935d4d138db70940c09e |
# Dataset Card for Evaluation run of PotatoOff/HamSter-0.1
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [PotatoOff/HamSter-0.1](https://huggingface.co/PotatoOff/HamSter-0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_PotatoOff__HamSter-0.1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T21:11:37.945575](https://huggingface.co/datasets/open-llm-leaderboard/details_PotatoOff__HamSter-0.1/blob/main/results_2024-01-13T21-11-37.945575.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.4276097079092565,
"acc_stderr": 0.034155225676891506,
"acc_norm": 0.4351931278757911,
"acc_norm_stderr": 0.03507757220797634,
"mc1": 0.3402692778457772,
"mc1_stderr": 0.016586304901762557,
"mc2": 0.5124400262486783,
"mc2_stderr": 0.016311439508262023
},
"harness|arc:challenge|25": {
"acc": 0.4351535836177474,
"acc_stderr": 0.014487986197186047,
"acc_norm": 0.46928327645051193,
"acc_norm_stderr": 0.014583792546304038
},
"harness|hellaswag|10": {
"acc": 0.5039832702648874,
"acc_stderr": 0.004989623068778789,
"acc_norm": 0.6808404700258912,
"acc_norm_stderr": 0.004651982864043485
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.42962962962962964,
"acc_stderr": 0.042763494943765995,
"acc_norm": 0.42962962962962964,
"acc_norm_stderr": 0.042763494943765995
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.39473684210526316,
"acc_stderr": 0.039777499346220734,
"acc_norm": 0.39473684210526316,
"acc_norm_stderr": 0.039777499346220734
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.41,
"acc_stderr": 0.04943110704237103,
"acc_norm": 0.41,
"acc_norm_stderr": 0.04943110704237103
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.44528301886792454,
"acc_stderr": 0.030588052974270655,
"acc_norm": 0.44528301886792454,
"acc_norm_stderr": 0.030588052974270655
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.4583333333333333,
"acc_stderr": 0.04166666666666665,
"acc_norm": 0.4583333333333333,
"acc_norm_stderr": 0.04166666666666665
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.35,
"acc_stderr": 0.0479372485441102,
"acc_norm": 0.35,
"acc_norm_stderr": 0.0479372485441102
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.3468208092485549,
"acc_stderr": 0.03629146670159663,
"acc_norm": 0.3468208092485549,
"acc_norm_stderr": 0.03629146670159663
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.24509803921568626,
"acc_stderr": 0.042801058373643966,
"acc_norm": 0.24509803921568626,
"acc_norm_stderr": 0.042801058373643966
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.49,
"acc_stderr": 0.05024183937956911,
"acc_norm": 0.49,
"acc_norm_stderr": 0.05024183937956911
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.3829787234042553,
"acc_stderr": 0.03177821250236922,
"acc_norm": 0.3829787234042553,
"acc_norm_stderr": 0.03177821250236922
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.2894736842105263,
"acc_stderr": 0.042663394431593935,
"acc_norm": 0.2894736842105263,
"acc_norm_stderr": 0.042663394431593935
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.43448275862068964,
"acc_stderr": 0.04130740879555497,
"acc_norm": 0.43448275862068964,
"acc_norm_stderr": 0.04130740879555497
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.3201058201058201,
"acc_stderr": 0.024026846392873502,
"acc_norm": 0.3201058201058201,
"acc_norm_stderr": 0.024026846392873502
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.23809523809523808,
"acc_stderr": 0.03809523809523811,
"acc_norm": 0.23809523809523808,
"acc_norm_stderr": 0.03809523809523811
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.29,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.29,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.4870967741935484,
"acc_stderr": 0.028434533152681855,
"acc_norm": 0.4870967741935484,
"acc_norm_stderr": 0.028434533152681855
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.3497536945812808,
"acc_stderr": 0.03355400904969566,
"acc_norm": 0.3497536945812808,
"acc_norm_stderr": 0.03355400904969566
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620332,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.5515151515151515,
"acc_stderr": 0.03883565977956929,
"acc_norm": 0.5515151515151515,
"acc_norm_stderr": 0.03883565977956929
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.5808080808080808,
"acc_stderr": 0.03515520728670417,
"acc_norm": 0.5808080808080808,
"acc_norm_stderr": 0.03515520728670417
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.5595854922279793,
"acc_stderr": 0.03582724530036094,
"acc_norm": 0.5595854922279793,
"acc_norm_stderr": 0.03582724530036094
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.4025641025641026,
"acc_stderr": 0.02486499515976776,
"acc_norm": 0.4025641025641026,
"acc_norm_stderr": 0.02486499515976776
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.2814814814814815,
"acc_stderr": 0.027420019350945277,
"acc_norm": 0.2814814814814815,
"acc_norm_stderr": 0.027420019350945277
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.3865546218487395,
"acc_stderr": 0.0316314580755238,
"acc_norm": 0.3865546218487395,
"acc_norm_stderr": 0.0316314580755238
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.2847682119205298,
"acc_stderr": 0.03684881521389024,
"acc_norm": 0.2847682119205298,
"acc_norm_stderr": 0.03684881521389024
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.5100917431192661,
"acc_stderr": 0.021432956203453313,
"acc_norm": 0.5100917431192661,
"acc_norm_stderr": 0.021432956203453313
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.32407407407407407,
"acc_stderr": 0.03191923445686186,
"acc_norm": 0.32407407407407407,
"acc_norm_stderr": 0.03191923445686186
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.5,
"acc_stderr": 0.03509312031717982,
"acc_norm": 0.5,
"acc_norm_stderr": 0.03509312031717982
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.5738396624472574,
"acc_stderr": 0.03219035703131774,
"acc_norm": 0.5738396624472574,
"acc_norm_stderr": 0.03219035703131774
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.45739910313901344,
"acc_stderr": 0.033435777055830646,
"acc_norm": 0.45739910313901344,
"acc_norm_stderr": 0.033435777055830646
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.46564885496183206,
"acc_stderr": 0.043749285605997376,
"acc_norm": 0.46564885496183206,
"acc_norm_stderr": 0.043749285605997376
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.5702479338842975,
"acc_stderr": 0.04519082021319771,
"acc_norm": 0.5702479338842975,
"acc_norm_stderr": 0.04519082021319771
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.49074074074074076,
"acc_stderr": 0.04832853553437055,
"acc_norm": 0.49074074074074076,
"acc_norm_stderr": 0.04832853553437055
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.4723926380368098,
"acc_stderr": 0.0392237829061099,
"acc_norm": 0.4723926380368098,
"acc_norm_stderr": 0.0392237829061099
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.29464285714285715,
"acc_stderr": 0.043270409325787296,
"acc_norm": 0.29464285714285715,
"acc_norm_stderr": 0.043270409325787296
},
"harness|hendrycksTest-management|5": {
"acc": 0.5242718446601942,
"acc_stderr": 0.04944901092973779,
"acc_norm": 0.5242718446601942,
"acc_norm_stderr": 0.04944901092973779
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.7264957264957265,
"acc_stderr": 0.02920254015343117,
"acc_norm": 0.7264957264957265,
"acc_norm_stderr": 0.02920254015343117
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.49,
"acc_stderr": 0.050241839379569095,
"acc_norm": 0.49,
"acc_norm_stderr": 0.050241839379569095
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.5938697318007663,
"acc_stderr": 0.017562037406478912,
"acc_norm": 0.5938697318007663,
"acc_norm_stderr": 0.017562037406478912
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.407514450867052,
"acc_stderr": 0.026454578146931498,
"acc_norm": 0.407514450867052,
"acc_norm_stderr": 0.026454578146931498
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.264804469273743,
"acc_stderr": 0.014756906483260664,
"acc_norm": 0.264804469273743,
"acc_norm_stderr": 0.014756906483260664
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.4411764705882353,
"acc_stderr": 0.028431095444176643,
"acc_norm": 0.4411764705882353,
"acc_norm_stderr": 0.028431095444176643
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.4405144694533762,
"acc_stderr": 0.028196400574197426,
"acc_norm": 0.4405144694533762,
"acc_norm_stderr": 0.028196400574197426
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.4567901234567901,
"acc_stderr": 0.02771666165019404,
"acc_norm": 0.4567901234567901,
"acc_norm_stderr": 0.02771666165019404
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.32978723404255317,
"acc_stderr": 0.0280459469420424,
"acc_norm": 0.32978723404255317,
"acc_norm_stderr": 0.0280459469420424
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.3239895697522816,
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"acc_norm": 0.3239895697522816,
"acc_norm_stderr": 0.011952840809646575
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.2536764705882353,
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"acc_norm": 0.2536764705882353,
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},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.38235294117647056,
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"acc_norm_stderr": 0.019659922493623336
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.5636363636363636,
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"acc_norm": 0.5636363636363636,
"acc_norm_stderr": 0.04750185058907296
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.5306122448979592,
"acc_stderr": 0.031949171367580624,
"acc_norm": 0.5306122448979592,
"acc_norm_stderr": 0.031949171367580624
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.5522388059701493,
"acc_stderr": 0.03516184772952166,
"acc_norm": 0.5522388059701493,
"acc_norm_stderr": 0.03516184772952166
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.78,
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"acc_norm": 0.78,
"acc_norm_stderr": 0.04163331998932261
},
"harness|hendrycksTest-virology|5": {
"acc": 0.3373493975903614,
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"acc_norm": 0.3373493975903614,
"acc_norm_stderr": 0.0368078369072758
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.5789473684210527,
"acc_stderr": 0.03786720706234214,
"acc_norm": 0.5789473684210527,
"acc_norm_stderr": 0.03786720706234214
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3402692778457772,
"mc1_stderr": 0.016586304901762557,
"mc2": 0.5124400262486783,
"mc2_stderr": 0.016311439508262023
},
"harness|winogrande|5": {
"acc": 0.6187845303867403,
"acc_stderr": 0.013650172164160318
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_PotatoOff__HamSter-0.1 | [
"region:us"
] | 2024-01-13T21:13:54+00:00 | {"pretty_name": "Evaluation run of PotatoOff/HamSter-0.1", "dataset_summary": "Dataset automatically created during the evaluation run of model [PotatoOff/HamSter-0.1](https://huggingface.co/PotatoOff/HamSter-0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_PotatoOff__HamSter-0.1\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T21:11:37.945575](https://huggingface.co/datasets/open-llm-leaderboard/details_PotatoOff__HamSter-0.1/blob/main/results_2024-01-13T21-11-37.945575.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.4276097079092565,\n \"acc_stderr\": 0.034155225676891506,\n \"acc_norm\": 0.4351931278757911,\n \"acc_norm_stderr\": 0.03507757220797634,\n \"mc1\": 0.3402692778457772,\n \"mc1_stderr\": 0.016586304901762557,\n \"mc2\": 0.5124400262486783,\n \"mc2_stderr\": 0.016311439508262023\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.4351535836177474,\n \"acc_stderr\": 0.014487986197186047,\n \"acc_norm\": 0.46928327645051193,\n \"acc_norm_stderr\": 0.014583792546304038\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5039832702648874,\n \"acc_stderr\": 0.004989623068778789,\n \"acc_norm\": 0.6808404700258912,\n \"acc_norm_stderr\": 0.004651982864043485\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.42962962962962964,\n \"acc_stderr\": 0.042763494943765995,\n \"acc_norm\": 0.42962962962962964,\n \"acc_norm_stderr\": 0.042763494943765995\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.39473684210526316,\n \"acc_stderr\": 0.039777499346220734,\n \"acc_norm\": 0.39473684210526316,\n \"acc_norm_stderr\": 0.039777499346220734\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237103,\n \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237103\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.44528301886792454,\n \"acc_stderr\": 0.030588052974270655,\n \"acc_norm\": 0.44528301886792454,\n \"acc_norm_stderr\": 0.030588052974270655\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4583333333333333,\n \"acc_stderr\": 0.04166666666666665,\n \"acc_norm\": 0.4583333333333333,\n \"acc_norm_stderr\": 0.04166666666666665\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.3468208092485549,\n \"acc_stderr\": 0.03629146670159663,\n \"acc_norm\": 0.3468208092485549,\n \"acc_norm_stderr\": 0.03629146670159663\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.24509803921568626,\n \"acc_stderr\": 0.042801058373643966,\n \"acc_norm\": 0.24509803921568626,\n \"acc_norm_stderr\": 0.042801058373643966\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.3829787234042553,\n \"acc_stderr\": 0.03177821250236922,\n \"acc_norm\": 0.3829787234042553,\n \"acc_norm_stderr\": 0.03177821250236922\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2894736842105263,\n \"acc_stderr\": 0.042663394431593935,\n \"acc_norm\": 0.2894736842105263,\n \"acc_norm_stderr\": 0.042663394431593935\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.43448275862068964,\n \"acc_stderr\": 0.04130740879555497,\n \"acc_norm\": 0.43448275862068964,\n \"acc_norm_stderr\": 0.04130740879555497\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.3201058201058201,\n \"acc_stderr\": 0.024026846392873502,\n \"acc_norm\": 0.3201058201058201,\n \"acc_norm_stderr\": 0.024026846392873502\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.23809523809523808,\n \"acc_stderr\": 0.03809523809523811,\n \"acc_norm\": 0.23809523809523808,\n \"acc_norm_stderr\": 0.03809523809523811\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.4870967741935484,\n \"acc_stderr\": 0.028434533152681855,\n \"acc_norm\": 0.4870967741935484,\n \"acc_norm_stderr\": 0.028434533152681855\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.3497536945812808,\n \"acc_stderr\": 0.03355400904969566,\n \"acc_norm\": 0.3497536945812808,\n \"acc_norm_stderr\": 0.03355400904969566\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.5515151515151515,\n \"acc_stderr\": 0.03883565977956929,\n \"acc_norm\": 0.5515151515151515,\n \"acc_norm_stderr\": 0.03883565977956929\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.5808080808080808,\n \"acc_stderr\": 0.03515520728670417,\n \"acc_norm\": 0.5808080808080808,\n \"acc_norm_stderr\": 0.03515520728670417\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.5595854922279793,\n \"acc_stderr\": 0.03582724530036094,\n \"acc_norm\": 0.5595854922279793,\n \"acc_norm_stderr\": 0.03582724530036094\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.4025641025641026,\n \"acc_stderr\": 0.02486499515976776,\n \"acc_norm\": 0.4025641025641026,\n \"acc_norm_stderr\": 0.02486499515976776\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.2814814814814815,\n \"acc_stderr\": 0.027420019350945277,\n \"acc_norm\": 0.2814814814814815,\n \"acc_norm_stderr\": 0.027420019350945277\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.3865546218487395,\n \"acc_stderr\": 0.0316314580755238,\n \"acc_norm\": 0.3865546218487395,\n \"acc_norm_stderr\": 0.0316314580755238\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.2847682119205298,\n \"acc_stderr\": 0.03684881521389024,\n \"acc_norm\": 0.2847682119205298,\n \"acc_norm_stderr\": 0.03684881521389024\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.5100917431192661,\n \"acc_stderr\": 0.021432956203453313,\n \"acc_norm\": 0.5100917431192661,\n \"acc_norm_stderr\": 0.021432956203453313\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.32407407407407407,\n \"acc_stderr\": 0.03191923445686186,\n \"acc_norm\": 0.32407407407407407,\n \"acc_norm_stderr\": 0.03191923445686186\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.03509312031717982,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.03509312031717982\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.5738396624472574,\n \"acc_stderr\": 0.03219035703131774,\n \"acc_norm\": 0.5738396624472574,\n \"acc_norm_stderr\": 0.03219035703131774\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.45739910313901344,\n \"acc_stderr\": 0.033435777055830646,\n \"acc_norm\": 0.45739910313901344,\n \"acc_norm_stderr\": 0.033435777055830646\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.46564885496183206,\n \"acc_stderr\": 0.043749285605997376,\n \"acc_norm\": 0.46564885496183206,\n \"acc_norm_stderr\": 0.043749285605997376\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.5702479338842975,\n \"acc_stderr\": 0.04519082021319771,\n \"acc_norm\": 0.5702479338842975,\n \"acc_norm_stderr\": 0.04519082021319771\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.49074074074074076,\n \"acc_stderr\": 0.04832853553437055,\n \"acc_norm\": 0.49074074074074076,\n \"acc_norm_stderr\": 0.04832853553437055\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.4723926380368098,\n \"acc_stderr\": 0.0392237829061099,\n \"acc_norm\": 0.4723926380368098,\n \"acc_norm_stderr\": 0.0392237829061099\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.29464285714285715,\n \"acc_stderr\": 0.043270409325787296,\n \"acc_norm\": 0.29464285714285715,\n \"acc_norm_stderr\": 0.043270409325787296\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.5242718446601942,\n \"acc_stderr\": 0.04944901092973779,\n \"acc_norm\": 0.5242718446601942,\n \"acc_norm_stderr\": 0.04944901092973779\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7264957264957265,\n \"acc_stderr\": 0.02920254015343117,\n \"acc_norm\": 0.7264957264957265,\n \"acc_norm_stderr\": 0.02920254015343117\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.49,\n \"acc_stderr\": 0.050241839379569095,\n \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.050241839379569095\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.5938697318007663,\n \"acc_stderr\": 0.017562037406478912,\n \"acc_norm\": 0.5938697318007663,\n \"acc_norm_stderr\": 0.017562037406478912\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.407514450867052,\n \"acc_stderr\": 0.026454578146931498,\n \"acc_norm\": 0.407514450867052,\n \"acc_norm_stderr\": 0.026454578146931498\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.264804469273743,\n \"acc_stderr\": 0.014756906483260664,\n \"acc_norm\": 0.264804469273743,\n \"acc_norm_stderr\": 0.014756906483260664\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.028431095444176643,\n \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.028431095444176643\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.4405144694533762,\n \"acc_stderr\": 0.028196400574197426,\n \"acc_norm\": 0.4405144694533762,\n \"acc_norm_stderr\": 0.028196400574197426\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.4567901234567901,\n \"acc_stderr\": 0.02771666165019404,\n \"acc_norm\": 0.4567901234567901,\n \"acc_norm_stderr\": 0.02771666165019404\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.32978723404255317,\n \"acc_stderr\": 0.0280459469420424,\n \"acc_norm\": 0.32978723404255317,\n \"acc_norm_stderr\": 0.0280459469420424\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3239895697522816,\n \"acc_stderr\": 0.011952840809646575,\n \"acc_norm\": 0.3239895697522816,\n \"acc_norm_stderr\": 0.011952840809646575\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.2536764705882353,\n \"acc_stderr\": 0.02643132987078953,\n \"acc_norm\": 0.2536764705882353,\n \"acc_norm_stderr\": 0.02643132987078953\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.019659922493623336,\n \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.019659922493623336\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5636363636363636,\n \"acc_stderr\": 0.04750185058907296,\n \"acc_norm\": 0.5636363636363636,\n \"acc_norm_stderr\": 0.04750185058907296\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.5306122448979592,\n \"acc_stderr\": 0.031949171367580624,\n \"acc_norm\": 0.5306122448979592,\n \"acc_norm_stderr\": 0.031949171367580624\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.5522388059701493,\n \"acc_stderr\": 0.03516184772952166,\n \"acc_norm\": 0.5522388059701493,\n \"acc_norm_stderr\": 0.03516184772952166\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.78,\n \"acc_stderr\": 0.04163331998932261,\n \"acc_norm\": 0.78,\n \"acc_norm_stderr\": 0.04163331998932261\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.3373493975903614,\n \"acc_stderr\": 0.0368078369072758,\n \"acc_norm\": 0.3373493975903614,\n \"acc_norm_stderr\": 0.0368078369072758\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.5789473684210527,\n \"acc_stderr\": 0.03786720706234214,\n \"acc_norm\": 0.5789473684210527,\n \"acc_norm_stderr\": 0.03786720706234214\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3402692778457772,\n \"mc1_stderr\": 0.016586304901762557,\n \"mc2\": 0.5124400262486783,\n \"mc2_stderr\": 0.016311439508262023\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.6187845303867403,\n \"acc_stderr\": 0.013650172164160318\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n }\n}\n```", "repo_url": "https://huggingface.co/PotatoOff/HamSter-0.1", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2024_01_13T21_11_37.945575", "path": ["**/details_harness|arc:challenge|25_2024-01-13T21-11-37.945575.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-01-13T21-11-37.945575.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_01_13T21_11_37.945575", "path": ["**/details_harness|gsm8k|5_2024-01-13T21-11-37.945575.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-01-13T21-11-37.945575.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_01_13T21_11_37.945575", "path": ["**/details_harness|hellaswag|10_2024-01-13T21-11-37.945575.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-01-13T21-11-37.945575.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_01_13T21_11_37.945575", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T21-11-37.945575.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-01-13T21-11-37.945575.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-01-13T21-11-37.945575.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T21-11-37.945575.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T21-11-37.945575.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-01-13T21-11-37.945575.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T21-11-37.945575.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T21-11-37.945575.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T21-11-37.945575.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T21-11-37.945575.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-01-13T21-11-37.945575.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-01-13T21-11-37.945575.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T21-11-37.945575.parquet", "**/details_harness|hendrycksTest-econometrics|5_2024-01-13T21-11-37.945575.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T21-11-37.945575.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T21-11-37.945575.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T21-11-37.945575.parquet", "**/details_harness|hendrycksTest-global_facts|5_2024-01-13T21-11-37.945575.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T21-11-37.945575.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T21-11-37.945575.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T21-11-37.945575.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-13T21-11-37.945575.parquet", 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"harness_hendrycksTest_high_school_world_history_5", "data_files": [{"split": "2024_01_13T21_11_37.945575", "path": ["**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T21-11-37.945575.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T21-11-37.945575.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_aging_5", "data_files": [{"split": "2024_01_13T21_11_37.945575", "path": ["**/details_harness|hendrycksTest-human_aging|5_2024-01-13T21-11-37.945575.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-human_aging|5_2024-01-13T21-11-37.945575.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_sexuality_5", "data_files": [{"split": "2024_01_13T21_11_37.945575", "path": ["**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T21-11-37.945575.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T21-11-37.945575.parquet"]}]}, {"config_name": "harness_hendrycksTest_international_law_5", "data_files": [{"split": "2024_01_13T21_11_37.945575", "path": ["**/details_harness|hendrycksTest-international_law|5_2024-01-13T21-11-37.945575.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-international_law|5_2024-01-13T21-11-37.945575.parquet"]}]}, {"config_name": "harness_hendrycksTest_jurisprudence_5", "data_files": [{"split": "2024_01_13T21_11_37.945575", "path": ["**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T21-11-37.945575.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T21-11-37.945575.parquet"]}]}, {"config_name": "harness_hendrycksTest_logical_fallacies_5", "data_files": [{"split": "2024_01_13T21_11_37.945575", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T21-11-37.945575.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T21-11-37.945575.parquet"]}]}, {"config_name": "harness_hendrycksTest_machine_learning_5", "data_files": [{"split": "2024_01_13T21_11_37.945575", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T21-11-37.945575.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T21-11-37.945575.parquet"]}]}, {"config_name": "harness_hendrycksTest_management_5", "data_files": [{"split": "2024_01_13T21_11_37.945575", "path": ["**/details_harness|hendrycksTest-management|5_2024-01-13T21-11-37.945575.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-management|5_2024-01-13T21-11-37.945575.parquet"]}]}, {"config_name": "harness_hendrycksTest_marketing_5", "data_files": [{"split": "2024_01_13T21_11_37.945575", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-13T21-11-37.945575.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-13T21-11-37.945575.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2024_01_13T21_11_37.945575", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T21-11-37.945575.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T21-11-37.945575.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2024_01_13T21_11_37.945575", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T21-11-37.945575.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T21-11-37.945575.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2024_01_13T21_11_37.945575", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T21-11-37.945575.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T21-11-37.945575.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2024_01_13T21_11_37.945575", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T21-11-37.945575.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T21-11-37.945575.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2024_01_13T21_11_37.945575", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T21-11-37.945575.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T21-11-37.945575.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2024_01_13T21_11_37.945575", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T21-11-37.945575.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T21-11-37.945575.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2024_01_13T21_11_37.945575", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T21-11-37.945575.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T21-11-37.945575.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2024_01_13T21_11_37.945575", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T21-11-37.945575.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T21-11-37.945575.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2024_01_13T21_11_37.945575", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T21-11-37.945575.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T21-11-37.945575.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_01_13T21_11_37.945575", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T21-11-37.945575.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T21-11-37.945575.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_01_13T21_11_37.945575", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T21-11-37.945575.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T21-11-37.945575.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_01_13T21_11_37.945575", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T21-11-37.945575.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T21-11-37.945575.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_01_13T21_11_37.945575", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T21-11-37.945575.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T21-11-37.945575.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_01_13T21_11_37.945575", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T21-11-37.945575.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T21-11-37.945575.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_01_13T21_11_37.945575", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T21-11-37.945575.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T21-11-37.945575.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_13T21_11_37.945575", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T21-11-37.945575.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T21-11-37.945575.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_13T21_11_37.945575", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T21-11-37.945575.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T21-11-37.945575.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_13T21_11_37.945575", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T21-11-37.945575.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T21-11-37.945575.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T21_11_37.945575", "path": ["**/details_harness|winogrande|5_2024-01-13T21-11-37.945575.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T21-11-37.945575.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T21_11_37.945575", "path": ["results_2024-01-13T21-11-37.945575.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T21-11-37.945575.parquet"]}]}]} | 2024-01-13T21:14:15+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of PotatoOff/HamSter-0.1
Dataset automatically created during the evaluation run of model PotatoOff/HamSter-0.1 on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-13T21:11:37.945575(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of PotatoOff/HamSter-0.1\n\n\n\nDataset automatically created during the evaluation run of model PotatoOff/HamSter-0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T21:11:37.945575(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of PotatoOff/HamSter-0.1\n\n\n\nDataset automatically created during the evaluation run of model PotatoOff/HamSter-0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T21:11:37.945575(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
9bd92c6144993f93aad09bafd7cc1b040da9ce87 |
# Dataset Card for Evaluation run of shitshow123/TinyLlama-1.1B-ChatStrong-DPO-PPO
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [shitshow123/TinyLlama-1.1B-ChatStrong-DPO-PPO](https://huggingface.co/shitshow123/TinyLlama-1.1B-ChatStrong-DPO-PPO) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_shitshow123__TinyLlama-1.1B-ChatStrong-DPO-PPO",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T21:14:09.825375](https://huggingface.co/datasets/open-llm-leaderboard/details_shitshow123__TinyLlama-1.1B-ChatStrong-DPO-PPO/blob/main/results_2024-01-13T21-14-09.825375.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.24211167185537147,
"acc_stderr": 0.030313687760954437,
"acc_norm": 0.24298283398639836,
"acc_norm_stderr": 0.031121580752328053,
"mc1": 0.25458996328029376,
"mc1_stderr": 0.015250117079156482,
"mc2": 0.48873713948713277,
"mc2_stderr": 0.016987991365255056
},
"harness|arc:challenge|25": {
"acc": 0.23293515358361774,
"acc_stderr": 0.01235250704261739,
"acc_norm": 0.3037542662116041,
"acc_norm_stderr": 0.013438909184778757
},
"harness|hellaswag|10": {
"acc": 0.2568213503286198,
"acc_stderr": 0.004359871519639544,
"acc_norm": 0.2575184226249751,
"acc_norm_stderr": 0.004363736410689636
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.18,
"acc_stderr": 0.03861229196653695,
"acc_norm": 0.18,
"acc_norm_stderr": 0.03861229196653695
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.23703703703703705,
"acc_stderr": 0.03673731683969506,
"acc_norm": 0.23703703703703705,
"acc_norm_stderr": 0.03673731683969506
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.20394736842105263,
"acc_stderr": 0.032790004063100515,
"acc_norm": 0.20394736842105263,
"acc_norm_stderr": 0.032790004063100515
},
"harness|hendrycksTest-business_ethics|5": {
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}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## Dataset Structure
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Who are the source data producers?
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#### Annotation process
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#### Personal and Sensitive Information
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## Bias, Risks, and Limitations
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_shitshow123__TinyLlama-1.1B-ChatStrong-DPO-PPO | [
"region:us"
] | 2024-01-13T21:15:56+00:00 | {"pretty_name": "Evaluation run of shitshow123/TinyLlama-1.1B-ChatStrong-DPO-PPO", "dataset_summary": "Dataset automatically created during the evaluation run of model [shitshow123/TinyLlama-1.1B-ChatStrong-DPO-PPO](https://huggingface.co/shitshow123/TinyLlama-1.1B-ChatStrong-DPO-PPO) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_shitshow123__TinyLlama-1.1B-ChatStrong-DPO-PPO\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T21:14:09.825375](https://huggingface.co/datasets/open-llm-leaderboard/details_shitshow123__TinyLlama-1.1B-ChatStrong-DPO-PPO/blob/main/results_2024-01-13T21-14-09.825375.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.24211167185537147,\n \"acc_stderr\": 0.030313687760954437,\n \"acc_norm\": 0.24298283398639836,\n \"acc_norm_stderr\": 0.031121580752328053,\n \"mc1\": 0.25458996328029376,\n \"mc1_stderr\": 0.015250117079156482,\n \"mc2\": 0.48873713948713277,\n \"mc2_stderr\": 0.016987991365255056\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.23293515358361774,\n \"acc_stderr\": 0.01235250704261739,\n \"acc_norm\": 0.3037542662116041,\n \"acc_norm_stderr\": 0.013438909184778757\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.2568213503286198,\n \"acc_stderr\": 0.004359871519639544,\n \"acc_norm\": 0.2575184226249751,\n \"acc_norm_stderr\": 0.004363736410689636\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.18,\n \"acc_stderr\": 0.03861229196653695,\n \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.03861229196653695\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.23703703703703705,\n \"acc_stderr\": 0.03673731683969506,\n \"acc_norm\": 0.23703703703703705,\n \"acc_norm_stderr\": 0.03673731683969506\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.20394736842105263,\n \"acc_stderr\": 0.032790004063100515,\n \"acc_norm\": 0.20394736842105263,\n \"acc_norm_stderr\": 0.032790004063100515\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.2641509433962264,\n \"acc_stderr\": 0.027134291628741713,\n \"acc_norm\": 0.2641509433962264,\n \"acc_norm_stderr\": 0.027134291628741713\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.25,\n \"acc_stderr\": 0.03621034121889507,\n \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.03621034121889507\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.26,\n \"acc_stderr\": 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"path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-13T21-14-09.825375.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2024_01_13T21_14_09.825375", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T21-14-09.825375.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T21-14-09.825375.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2024_01_13T21_14_09.825375", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T21-14-09.825375.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T21-14-09.825375.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2024_01_13T21_14_09.825375", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T21-14-09.825375.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T21-14-09.825375.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2024_01_13T21_14_09.825375", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T21-14-09.825375.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T21-14-09.825375.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2024_01_13T21_14_09.825375", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T21-14-09.825375.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T21-14-09.825375.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2024_01_13T21_14_09.825375", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T21-14-09.825375.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T21-14-09.825375.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2024_01_13T21_14_09.825375", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T21-14-09.825375.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T21-14-09.825375.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2024_01_13T21_14_09.825375", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T21-14-09.825375.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T21-14-09.825375.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2024_01_13T21_14_09.825375", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T21-14-09.825375.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T21-14-09.825375.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_01_13T21_14_09.825375", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T21-14-09.825375.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T21-14-09.825375.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_01_13T21_14_09.825375", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T21-14-09.825375.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T21-14-09.825375.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_01_13T21_14_09.825375", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T21-14-09.825375.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T21-14-09.825375.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_01_13T21_14_09.825375", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T21-14-09.825375.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T21-14-09.825375.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_01_13T21_14_09.825375", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T21-14-09.825375.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T21-14-09.825375.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_01_13T21_14_09.825375", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T21-14-09.825375.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T21-14-09.825375.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_13T21_14_09.825375", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T21-14-09.825375.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T21-14-09.825375.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_13T21_14_09.825375", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T21-14-09.825375.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T21-14-09.825375.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_13T21_14_09.825375", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T21-14-09.825375.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T21-14-09.825375.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T21_14_09.825375", "path": ["**/details_harness|winogrande|5_2024-01-13T21-14-09.825375.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T21-14-09.825375.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T21_14_09.825375", "path": ["results_2024-01-13T21-14-09.825375.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T21-14-09.825375.parquet"]}]}]} | 2024-01-13T21:16:18+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of shitshow123/TinyLlama-1.1B-ChatStrong-DPO-PPO
Dataset automatically created during the evaluation run of model shitshow123/TinyLlama-1.1B-ChatStrong-DPO-PPO on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-13T21:14:09.825375(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of shitshow123/TinyLlama-1.1B-ChatStrong-DPO-PPO\n\n\n\nDataset automatically created during the evaluation run of model shitshow123/TinyLlama-1.1B-ChatStrong-DPO-PPO on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T21:14:09.825375(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of shitshow123/TinyLlama-1.1B-ChatStrong-DPO-PPO\n\n\n\nDataset automatically created during the evaluation run of model shitshow123/TinyLlama-1.1B-ChatStrong-DPO-PPO on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T21:14:09.825375(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
a63cb114c39e79b1a8a1803c98060e133d0c4b78 |
# Dataset of m3/M3/M3 (Girls' Frontline)
This is the dataset of m3/M3/M3 (Girls' Frontline), containing 18 images and their tags.
The core tags of this character are `blonde_hair, purple_eyes, short_hair, bangs, long_hair, breasts, ahoge`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 18 | 22.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/m3_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 18 | 12.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/m3_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 42 | 26.11 MiB | [Download](https://huggingface.co/datasets/CyberHarem/m3_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 18 | 19.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/m3_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 42 | 37.80 MiB | [Download](https://huggingface.co/datasets/CyberHarem/m3_girlsfrontline/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/m3_girlsfrontline',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 18 |  |  |  |  |  | 1girl, solo, looking_at_viewer, white_shirt, simple_background, blush, closed_mouth, holding, long_sleeves, military_uniform, belt, black_necktie, collared_shirt, jacket, white_background, gun, skirt, smile |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | white_shirt | simple_background | blush | closed_mouth | holding | long_sleeves | military_uniform | belt | black_necktie | collared_shirt | jacket | white_background | gun | skirt | smile |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:--------------|:--------------------|:--------|:---------------|:----------|:---------------|:-------------------|:-------|:----------------|:-----------------|:---------|:-------------------|:------|:--------|:--------|
| 0 | 18 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/m3_girlsfrontline | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T21:21:22+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T21:25:53+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of m3/M3/M3 (Girls' Frontline)
======================================
This is the dataset of m3/M3/M3 (Girls' Frontline), containing 18 images and their tags.
The core tags of this character are 'blonde\_hair, purple\_eyes, short\_hair, bangs, long\_hair, breasts, ahoge', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
eacf77babfbd7836443e1f3a7cbc0bacd23dac21 |
# Dataset of libeccio/リベッチオ/西南风 (Azur Lane)
This is the dataset of libeccio/リベッチオ/西南风 (Azur Lane), containing 24 images and their tags.
The core tags of this character are `blue_eyes, white_hair, long_hair, hat, beret, breasts, bangs, multicolored_hair, blue_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 24 | 32.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/libeccio_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 24 | 17.84 MiB | [Download](https://huggingface.co/datasets/CyberHarem/libeccio_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 45 | 32.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/libeccio_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 24 | 28.23 MiB | [Download](https://huggingface.co/datasets/CyberHarem/libeccio_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 45 | 49.85 MiB | [Download](https://huggingface.co/datasets/CyberHarem/libeccio_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/libeccio_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 9 |  |  |  |  |  | 1girl, blush, looking_at_viewer, smile, solo, long_sleeves, ahoge, bow, white_background, white_dress, closed_mouth, green_jacket, open_jacket, ribbon, black_footwear, green_headwear, hair_between_eyes, simple_background, small_breasts, standing |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | looking_at_viewer | smile | solo | long_sleeves | ahoge | bow | white_background | white_dress | closed_mouth | green_jacket | open_jacket | ribbon | black_footwear | green_headwear | hair_between_eyes | simple_background | small_breasts | standing |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:--------------------|:--------|:-------|:---------------|:--------|:------|:-------------------|:--------------|:---------------|:---------------|:--------------|:---------|:-----------------|:-----------------|:--------------------|:--------------------|:----------------|:-----------|
| 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/libeccio_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T21:22:16+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T21:29:27+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of libeccio/リベッチオ/西南风 (Azur Lane)
=========================================
This is the dataset of libeccio/リベッチオ/西南风 (Azur Lane), containing 24 images and their tags.
The core tags of this character are 'blue\_eyes, white\_hair, long\_hair, hat, beret, breasts, bangs, multicolored\_hair, blue\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
5a286e3c175abcc6b3eab403159d0c5c97c20be5 |
# Dataset of agano/阿賀野/阿贺野 (Azur Lane)
This is the dataset of agano/阿賀野/阿贺野 (Azur Lane), containing 32 images and their tags.
The core tags of this character are `breasts, long_hair, red_eyes, black_hair, bangs, large_breasts, very_long_hair, ponytail, hair_ornament, ahoge, bow, hair_bow, red_bow`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 32 | 44.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/agano_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 32 | 26.67 MiB | [Download](https://huggingface.co/datasets/CyberHarem/agano_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 78 | 52.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/agano_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 32 | 39.95 MiB | [Download](https://huggingface.co/datasets/CyberHarem/agano_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 78 | 72.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/agano_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/agano_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 10 |  |  |  |  |  | 1girl, bare_shoulders, cleavage, collarbone, solo, looking_at_viewer, smile, blush, detached_sleeves, wide_sleeves, simple_background, black_pantyhose, black_skirt, kimono, obi, pleated_skirt, ribbon_trim, white_background, closed_mouth, open_mouth |
| 1 | 10 |  |  |  |  |  | blush, looking_at_viewer, 1girl, smile, solo, brown_coat, closed_mouth, black_pantyhose, hair_ribbon, turtleneck_sweater, aran_sweater, open_coat, sweater_dress, bag, holding, long_sleeves, red_ribbon, sleeveless_turtleneck, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | cleavage | collarbone | solo | looking_at_viewer | smile | blush | detached_sleeves | wide_sleeves | simple_background | black_pantyhose | black_skirt | kimono | obi | pleated_skirt | ribbon_trim | white_background | closed_mouth | open_mouth | brown_coat | hair_ribbon | turtleneck_sweater | aran_sweater | open_coat | sweater_dress | bag | holding | long_sleeves | red_ribbon | sleeveless_turtleneck |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:-----------|:-------------|:-------|:--------------------|:--------|:--------|:-------------------|:---------------|:--------------------|:------------------|:--------------|:---------|:------|:----------------|:--------------|:-------------------|:---------------|:-------------|:-------------|:--------------|:---------------------|:---------------|:------------|:----------------|:------|:----------|:---------------|:-------------|:------------------------|
| 0 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | |
| 1 | 10 |  |  |  |  |  | X | | | | X | X | X | X | | | | X | | | | | | X | X | | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/agano_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T21:22:16+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T21:31:28+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of agano/阿賀野/阿贺野 (Azur Lane)
====================================
This is the dataset of agano/阿賀野/阿贺野 (Azur Lane), containing 32 images and their tags.
The core tags of this character are 'breasts, long\_hair, red\_eyes, black\_hair, bangs, large\_breasts, very\_long\_hair, ponytail, hair\_ornament, ahoge, bow, hair\_bow, red\_bow', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
29b68f60e696038a887d5cd9ec70b75a44b07762 |
# Dataset Card for Evaluation run of andysalerno/openchat-nectar-0.4
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [andysalerno/openchat-nectar-0.4](https://huggingface.co/andysalerno/openchat-nectar-0.4) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_andysalerno__openchat-nectar-0.4",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T21:31:07.575473](https://huggingface.co/datasets/open-llm-leaderboard/details_andysalerno__openchat-nectar-0.4/blob/main/results_2024-01-13T21-31-07.575473.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6548155823628143,
"acc_stderr": 0.03186572656130094,
"acc_norm": 0.6554787207794509,
"acc_norm_stderr": 0.0325212982695846,
"mc1": 0.3525091799265606,
"mc1_stderr": 0.016724646380756547,
"mc2": 0.5170655549832318,
"mc2_stderr": 0.015386835244454444
},
"harness|arc:challenge|25": {
"acc": 0.6305460750853242,
"acc_stderr": 0.01410457836649189,
"acc_norm": 0.6663822525597269,
"acc_norm_stderr": 0.01377868705417654
},
"harness|hellaswag|10": {
"acc": 0.6356303525194185,
"acc_stderr": 0.004802694106203655,
"acc_norm": 0.8323043218482374,
"acc_norm_stderr": 0.003728322968874899
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.35,
"acc_stderr": 0.0479372485441102,
"acc_norm": 0.35,
"acc_norm_stderr": 0.0479372485441102
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6444444444444445,
"acc_stderr": 0.04135176749720385,
"acc_norm": 0.6444444444444445,
"acc_norm_stderr": 0.04135176749720385
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6842105263157895,
"acc_stderr": 0.0378272898086547,
"acc_norm": 0.6842105263157895,
"acc_norm_stderr": 0.0378272898086547
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.66,
"acc_stderr": 0.04760952285695237,
"acc_norm": 0.66,
"acc_norm_stderr": 0.04760952285695237
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7094339622641509,
"acc_stderr": 0.02794321998933714,
"acc_norm": 0.7094339622641509,
"acc_norm_stderr": 0.02794321998933714
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7847222222222222,
"acc_stderr": 0.03437079344106136,
"acc_norm": 0.7847222222222222,
"acc_norm_stderr": 0.03437079344106136
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.47,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.47,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.57,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.57,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.38,
"acc_stderr": 0.04878317312145634,
"acc_norm": 0.38,
"acc_norm_stderr": 0.04878317312145634
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6878612716763006,
"acc_stderr": 0.03533133389323657,
"acc_norm": 0.6878612716763006,
"acc_norm_stderr": 0.03533133389323657
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4019607843137255,
"acc_stderr": 0.048786087144669955,
"acc_norm": 0.4019607843137255,
"acc_norm_stderr": 0.048786087144669955
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.72,
"acc_stderr": 0.045126085985421276,
"acc_norm": 0.72,
"acc_norm_stderr": 0.045126085985421276
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5829787234042553,
"acc_stderr": 0.03223276266711712,
"acc_norm": 0.5829787234042553,
"acc_norm_stderr": 0.03223276266711712
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.47368421052631576,
"acc_stderr": 0.04697085136647863,
"acc_norm": 0.47368421052631576,
"acc_norm_stderr": 0.04697085136647863
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.6,
"acc_stderr": 0.040824829046386284,
"acc_norm": 0.6,
"acc_norm_stderr": 0.040824829046386284
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.42857142857142855,
"acc_stderr": 0.025487187147859375,
"acc_norm": 0.42857142857142855,
"acc_norm_stderr": 0.025487187147859375
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.5,
"acc_stderr": 0.04472135954999579,
"acc_norm": 0.5,
"acc_norm_stderr": 0.04472135954999579
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7870967741935484,
"acc_stderr": 0.02328766512726854,
"acc_norm": 0.7870967741935484,
"acc_norm_stderr": 0.02328766512726854
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5073891625615764,
"acc_stderr": 0.035176035403610105,
"acc_norm": 0.5073891625615764,
"acc_norm_stderr": 0.035176035403610105
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.7,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.793939393939394,
"acc_stderr": 0.03158415324047711,
"acc_norm": 0.793939393939394,
"acc_norm_stderr": 0.03158415324047711
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.02962022787479048,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.02962022787479048
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8963730569948186,
"acc_stderr": 0.02199531196364424,
"acc_norm": 0.8963730569948186,
"acc_norm_stderr": 0.02199531196364424
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6717948717948717,
"acc_stderr": 0.023807633198657266,
"acc_norm": 0.6717948717948717,
"acc_norm_stderr": 0.023807633198657266
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3592592592592593,
"acc_stderr": 0.029252905927251972,
"acc_norm": 0.3592592592592593,
"acc_norm_stderr": 0.029252905927251972
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6764705882352942,
"acc_stderr": 0.03038835355188679,
"acc_norm": 0.6764705882352942,
"acc_norm_stderr": 0.03038835355188679
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3443708609271523,
"acc_stderr": 0.03879687024073327,
"acc_norm": 0.3443708609271523,
"acc_norm_stderr": 0.03879687024073327
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8513761467889909,
"acc_stderr": 0.015251253773660836,
"acc_norm": 0.8513761467889909,
"acc_norm_stderr": 0.015251253773660836
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5185185185185185,
"acc_stderr": 0.0340763209385405,
"acc_norm": 0.5185185185185185,
"acc_norm_stderr": 0.0340763209385405
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8284313725490197,
"acc_stderr": 0.026460569561240644,
"acc_norm": 0.8284313725490197,
"acc_norm_stderr": 0.026460569561240644
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.810126582278481,
"acc_stderr": 0.025530100460233504,
"acc_norm": 0.810126582278481,
"acc_norm_stderr": 0.025530100460233504
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.7174887892376681,
"acc_stderr": 0.03021683101150878,
"acc_norm": 0.7174887892376681,
"acc_norm_stderr": 0.03021683101150878
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7709923664122137,
"acc_stderr": 0.036853466317118506,
"acc_norm": 0.7709923664122137,
"acc_norm_stderr": 0.036853466317118506
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8181818181818182,
"acc_stderr": 0.03520893951097653,
"acc_norm": 0.8181818181818182,
"acc_norm_stderr": 0.03520893951097653
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7685185185185185,
"acc_stderr": 0.04077494709252627,
"acc_norm": 0.7685185185185185,
"acc_norm_stderr": 0.04077494709252627
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7730061349693251,
"acc_stderr": 0.03291099578615769,
"acc_norm": 0.7730061349693251,
"acc_norm_stderr": 0.03291099578615769
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.44642857142857145,
"acc_stderr": 0.04718471485219588,
"acc_norm": 0.44642857142857145,
"acc_norm_stderr": 0.04718471485219588
},
"harness|hendrycksTest-management|5": {
"acc": 0.8058252427184466,
"acc_stderr": 0.03916667762822584,
"acc_norm": 0.8058252427184466,
"acc_norm_stderr": 0.03916667762822584
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8931623931623932,
"acc_stderr": 0.02023714900899093,
"acc_norm": 0.8931623931623932,
"acc_norm_stderr": 0.02023714900899093
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.77,
"acc_stderr": 0.04229525846816505,
"acc_norm": 0.77,
"acc_norm_stderr": 0.04229525846816505
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8378033205619413,
"acc_stderr": 0.013182222616720893,
"acc_norm": 0.8378033205619413,
"acc_norm_stderr": 0.013182222616720893
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7572254335260116,
"acc_stderr": 0.023083658586984204,
"acc_norm": 0.7572254335260116,
"acc_norm_stderr": 0.023083658586984204
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.23910614525139665,
"acc_stderr": 0.014265554192331154,
"acc_norm": 0.23910614525139665,
"acc_norm_stderr": 0.014265554192331154
},
"harness|hendrycksTest-nutrition|5": {
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"acc_norm": 0.7581699346405228,
"acc_norm_stderr": 0.024518195641879334
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7170418006430869,
"acc_stderr": 0.02558306248998482,
"acc_norm": 0.7170418006430869,
"acc_norm_stderr": 0.02558306248998482
},
"harness|hendrycksTest-prehistory|5": {
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"acc_stderr": 0.02357688174400572,
"acc_norm": 0.7654320987654321,
"acc_norm_stderr": 0.02357688174400572
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.4787234042553192,
"acc_stderr": 0.029800481645628693,
"acc_norm": 0.4787234042553192,
"acc_norm_stderr": 0.029800481645628693
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"harness|hendrycksTest-professional_law|5": {
"acc": 0.4921773142112125,
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"acc_norm": 0.4921773142112125,
"acc_norm_stderr": 0.0127686730761119
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"harness|hendrycksTest-professional_medicine|5": {
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"acc_stderr": 0.026917481224377204,
"acc_norm": 0.7316176470588235,
"acc_norm_stderr": 0.026917481224377204
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"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6715686274509803,
"acc_stderr": 0.018999707383162666,
"acc_norm": 0.6715686274509803,
"acc_norm_stderr": 0.018999707383162666
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6545454545454545,
"acc_stderr": 0.04554619617541054,
"acc_norm": 0.6545454545454545,
"acc_norm_stderr": 0.04554619617541054
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7673469387755102,
"acc_stderr": 0.02704925791589618,
"acc_norm": 0.7673469387755102,
"acc_norm_stderr": 0.02704925791589618
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8507462686567164,
"acc_stderr": 0.02519692987482706,
"acc_norm": 0.8507462686567164,
"acc_norm_stderr": 0.02519692987482706
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.86,
"acc_stderr": 0.03487350880197769,
"acc_norm": 0.86,
"acc_norm_stderr": 0.03487350880197769
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5240963855421686,
"acc_stderr": 0.03887971849597264,
"acc_norm": 0.5240963855421686,
"acc_norm_stderr": 0.03887971849597264
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8245614035087719,
"acc_stderr": 0.029170885500727665,
"acc_norm": 0.8245614035087719,
"acc_norm_stderr": 0.029170885500727665
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3525091799265606,
"mc1_stderr": 0.016724646380756547,
"mc2": 0.5170655549832318,
"mc2_stderr": 0.015386835244454444
},
"harness|winogrande|5": {
"acc": 0.8168902920284136,
"acc_stderr": 0.01086977863316837
},
"harness|gsm8k|5": {
"acc": 0.686125852918878,
"acc_stderr": 0.01278268125105319
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Annotation process
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#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_andysalerno__openchat-nectar-0.4 | [
"region:us"
] | 2024-01-13T21:33:22+00:00 | {"pretty_name": "Evaluation run of andysalerno/openchat-nectar-0.4", "dataset_summary": "Dataset automatically created during the evaluation run of model [andysalerno/openchat-nectar-0.4](https://huggingface.co/andysalerno/openchat-nectar-0.4) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_andysalerno__openchat-nectar-0.4\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T21:31:07.575473](https://huggingface.co/datasets/open-llm-leaderboard/details_andysalerno__openchat-nectar-0.4/blob/main/results_2024-01-13T21-31-07.575473.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6548155823628143,\n \"acc_stderr\": 0.03186572656130094,\n \"acc_norm\": 0.6554787207794509,\n \"acc_norm_stderr\": 0.0325212982695846,\n \"mc1\": 0.3525091799265606,\n \"mc1_stderr\": 0.016724646380756547,\n \"mc2\": 0.5170655549832318,\n \"mc2_stderr\": 0.015386835244454444\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6305460750853242,\n \"acc_stderr\": 0.01410457836649189,\n \"acc_norm\": 0.6663822525597269,\n \"acc_norm_stderr\": 0.01377868705417654\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6356303525194185,\n \"acc_stderr\": 0.004802694106203655,\n \"acc_norm\": 0.8323043218482374,\n \"acc_norm_stderr\": 0.003728322968874899\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6444444444444445,\n \"acc_stderr\": 0.04135176749720385,\n \"acc_norm\": 0.6444444444444445,\n \"acc_norm_stderr\": 0.04135176749720385\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6842105263157895,\n \"acc_stderr\": 0.0378272898086547,\n \"acc_norm\": 0.6842105263157895,\n \"acc_norm_stderr\": 0.0378272898086547\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.7094339622641509,\n \"acc_stderr\": 0.02794321998933714,\n \"acc_norm\": 0.7094339622641509,\n \"acc_norm_stderr\": 0.02794321998933714\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7847222222222222,\n \"acc_stderr\": 0.03437079344106136,\n \"acc_norm\": 0.7847222222222222,\n \"acc_norm_stderr\": 0.03437079344106136\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.57,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145634,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145634\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6878612716763006,\n \"acc_stderr\": 0.03533133389323657,\n \"acc_norm\": 0.6878612716763006,\n \"acc_norm_stderr\": 0.03533133389323657\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.048786087144669955,\n \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.048786087144669955\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.72,\n \"acc_stderr\": 0.045126085985421276,\n \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.045126085985421276\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5829787234042553,\n \"acc_stderr\": 0.03223276266711712,\n \"acc_norm\": 0.5829787234042553,\n \"acc_norm_stderr\": 0.03223276266711712\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.47368421052631576,\n \"acc_stderr\": 0.04697085136647863,\n \"acc_norm\": 0.47368421052631576,\n \"acc_norm_stderr\": 0.04697085136647863\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.040824829046386284,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.040824829046386284\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.42857142857142855,\n \"acc_stderr\": 0.025487187147859375,\n \"acc_norm\": 0.42857142857142855,\n \"acc_norm_stderr\": 0.025487187147859375\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.04472135954999579,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.04472135954999579\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7870967741935484,\n \"acc_stderr\": 0.02328766512726854,\n \"acc_norm\": 0.7870967741935484,\n \"acc_norm_stderr\": 0.02328766512726854\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5073891625615764,\n \"acc_stderr\": 0.035176035403610105,\n \"acc_norm\": 0.5073891625615764,\n \"acc_norm_stderr\": 0.035176035403610105\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.793939393939394,\n \"acc_stderr\": 0.03158415324047711,\n \"acc_norm\": 0.793939393939394,\n \"acc_norm_stderr\": 0.03158415324047711\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.02962022787479048,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.02962022787479048\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.02199531196364424,\n \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.02199531196364424\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6717948717948717,\n \"acc_stderr\": 0.023807633198657266,\n \"acc_norm\": 0.6717948717948717,\n \"acc_norm_stderr\": 0.023807633198657266\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.3592592592592593,\n \"acc_stderr\": 0.029252905927251972,\n \"acc_norm\": 0.3592592592592593,\n \"acc_norm_stderr\": 0.029252905927251972\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.03038835355188679,\n \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.03038835355188679\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.3443708609271523,\n \"acc_stderr\": 0.03879687024073327,\n \"acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.03879687024073327\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8513761467889909,\n \"acc_stderr\": 0.015251253773660836,\n \"acc_norm\": 0.8513761467889909,\n \"acc_norm_stderr\": 0.015251253773660836\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5185185185185185,\n \"acc_stderr\": 0.0340763209385405,\n \"acc_norm\": 0.5185185185185185,\n \"acc_norm_stderr\": 0.0340763209385405\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8284313725490197,\n \"acc_stderr\": 0.026460569561240644,\n \"acc_norm\": 0.8284313725490197,\n \"acc_norm_stderr\": 0.026460569561240644\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.810126582278481,\n \"acc_stderr\": 0.025530100460233504,\n \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.025530100460233504\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7174887892376681,\n \"acc_stderr\": 0.03021683101150878,\n \"acc_norm\": 0.7174887892376681,\n \"acc_norm_stderr\": 0.03021683101150878\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.8181818181818182,\n \"acc_stderr\": 0.03520893951097653,\n \"acc_norm\": 0.8181818181818182,\n \"acc_norm_stderr\": 0.03520893951097653\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\n \"acc_stderr\": 0.04077494709252627,\n \"acc_norm\": 0.7685185185185185,\n \"acc_norm_stderr\": 0.04077494709252627\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.44642857142857145,\n \"acc_stderr\": 0.04718471485219588,\n \"acc_norm\": 0.44642857142857145,\n \"acc_norm_stderr\": 0.04718471485219588\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.8058252427184466,\n \"acc_stderr\": 0.03916667762822584,\n \"acc_norm\": 0.8058252427184466,\n \"acc_norm_stderr\": 0.03916667762822584\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8931623931623932,\n \"acc_stderr\": 0.02023714900899093,\n \"acc_norm\": 0.8931623931623932,\n \"acc_norm_stderr\": 0.02023714900899093\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.77,\n \"acc_stderr\": 0.04229525846816505,\n \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.04229525846816505\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8378033205619413,\n \"acc_stderr\": 0.013182222616720893,\n \"acc_norm\": 0.8378033205619413,\n \"acc_norm_stderr\": 0.013182222616720893\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7572254335260116,\n \"acc_stderr\": 0.023083658586984204,\n \"acc_norm\": 0.7572254335260116,\n \"acc_norm_stderr\": 0.023083658586984204\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23910614525139665,\n \"acc_stderr\": 0.014265554192331154,\n \"acc_norm\": 0.23910614525139665,\n \"acc_norm_stderr\": 0.014265554192331154\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.7581699346405228,\n \"acc_stderr\": 0.024518195641879334,\n \"acc_norm\": 0.7581699346405228,\n \"acc_norm_stderr\": 0.024518195641879334\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7170418006430869,\n \"acc_stderr\": 0.02558306248998482,\n \"acc_norm\": 0.7170418006430869,\n 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#region-us
|
# Dataset Card for Evaluation run of andysalerno/openchat-nectar-0.4
Dataset automatically created during the evaluation run of model andysalerno/openchat-nectar-0.4 on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-13T21:31:07.575473(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
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- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
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## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
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## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of andysalerno/openchat-nectar-0.4\n\n\n\nDataset automatically created during the evaluation run of model andysalerno/openchat-nectar-0.4 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T21:31:07.575473(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of andysalerno/openchat-nectar-0.4\n\n\n\nDataset automatically created during the evaluation run of model andysalerno/openchat-nectar-0.4 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T21:31:07.575473(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
4eada6230f32cc1c73061eca2675f36bb3e99184 |
An augmented and further cleaned version of [PIPPA-shareGPT](https://huggingface.co/datasets/kingbri/PIPPA-shareGPT) (specifically `pippa_sharegpt_trimmed.jsonl`, drawn from [PygmalianAI's PIPPA](https://huggingface.co/datasets/PygmalionAI/PIPPA)) in Fastchat format, modified in the following ways:
- The first prompt is modified to add context and simple references to aspects of the conversation (OOC, use of emojis, content).
- All {name} and {char} replaced by actual names and characters randomly generated by [Faker](https://pypi.org/project/Faker/).
- Very short conversations (<50 tokens) removed.
- Further de-duplicated, keeping the longest unique conversation.
- Conversations were made to be alternating (user/assistant), always starting with the user, and ending with the assistant. | grimulkan/PIPPA-augmented-dedup | [
"license:unknown",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T21:37:44+00:00 | {"license": "unknown", "tags": ["not-for-all-audiences"]} | 2024-01-24T00:00:39+00:00 | [] | [] | TAGS
#license-unknown #not-for-all-audiences #region-us
|
An augmented and further cleaned version of PIPPA-shareGPT (specifically 'pippa_sharegpt_trimmed.jsonl', drawn from PygmalianAI's PIPPA) in Fastchat format, modified in the following ways:
- The first prompt is modified to add context and simple references to aspects of the conversation (OOC, use of emojis, content).
- All {name} and {char} replaced by actual names and characters randomly generated by Faker.
- Very short conversations (<50 tokens) removed.
- Further de-duplicated, keeping the longest unique conversation.
- Conversations were made to be alternating (user/assistant), always starting with the user, and ending with the assistant. | [] | [
"TAGS\n#license-unknown #not-for-all-audiences #region-us \n"
] |
aa2c305bdff163bfe1ec3e1b9aa461eeb2f1e019 |
math_qa converted to Python snippets | euclaise/mathqa_programs | [
"license:apache-2.0",
"region:us"
] | 2024-01-13T21:42:41+00:00 | {"license": "apache-2.0", "dataset_info": {"features": [{"name": "options", "dtype": "string"}, {"name": "correct", "dtype": "string"}, {"name": "annotated_formula", "dtype": "string"}, {"name": "problem", "dtype": "string"}, {"name": "rationale", "dtype": "string"}, {"name": "program", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 17017833, "num_examples": 28851}], "download_size": 8877888, "dataset_size": 17017833}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-01-13T21:46:00+00:00 | [] | [] | TAGS
#license-apache-2.0 #region-us
|
math_qa converted to Python snippets | [] | [
"TAGS\n#license-apache-2.0 #region-us \n"
] |
73237d4b03c7fa06d0e5fe7725a0a478b2b7b776 |
# Dataset of foch/フォッシュ/福煦 (Azur Lane)
This is the dataset of foch/フォッシュ/福煦 (Azur Lane), containing 76 images and their tags.
The core tags of this character are `breasts, purple_hair, bangs, hair_between_eyes, multicolored_hair, long_hair, large_breasts, ahoge, crossed_bangs, grey_hair, red_eyes, pink_eyes, blue_hair, hair_ornament, sidelocks`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 76 | 113.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/foch_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 76 | 55.03 MiB | [Download](https://huggingface.co/datasets/CyberHarem/foch_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 177 | 116.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/foch_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 76 | 95.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/foch_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 177 | 175.93 MiB | [Download](https://huggingface.co/datasets/CyberHarem/foch_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/foch_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 9 |  |  |  |  |  | 1girl, solo, white_leotard, blush, cowboy_shot, cross_hair_ornament, epaulettes, long_sleeves, looking_at_viewer, simple_background, standing, thighhighs, white_background, cape, groin, highleg, jacket, open_mouth, smile |
| 1 | 5 |  |  |  |  |  | 1girl, bare_shoulders, black_shorts, cropped_sweater, looking_at_viewer, off-shoulder_sweater, official_alternate_costume, smile, solo, white_sweater, cowboy_shot, midriff, navel, open_mouth, white_background, blush, simple_background, two-tone_hair, bag, cleavage, long_sleeves, petals, standing |
| 2 | 7 |  |  |  |  |  | 1girl, bare_shoulders, black_shorts, collarbone, cropped_sweater, high-waist_shorts, looking_at_viewer, off-shoulder_sweater, official_alternate_costume, solo, standing, thigh_holster, white_sweater, blush, cleavage, handbag, sleeves_past_wrists, long_sleeves, parted_lips, shoulder_bag, smile, zipper_pull_tab, white_background, cowboy_shot, full_body, legs, shoes, two-tone_hair |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | white_leotard | blush | cowboy_shot | cross_hair_ornament | epaulettes | long_sleeves | looking_at_viewer | simple_background | standing | thighhighs | white_background | cape | groin | highleg | jacket | open_mouth | smile | bare_shoulders | black_shorts | cropped_sweater | off-shoulder_sweater | official_alternate_costume | white_sweater | midriff | navel | two-tone_hair | bag | cleavage | petals | collarbone | high-waist_shorts | thigh_holster | handbag | sleeves_past_wrists | parted_lips | shoulder_bag | zipper_pull_tab | full_body | legs | shoes |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:----------------|:--------|:--------------|:----------------------|:-------------|:---------------|:--------------------|:--------------------|:-----------|:-------------|:-------------------|:-------|:--------|:----------|:---------|:-------------|:--------|:-----------------|:---------------|:------------------|:-----------------------|:-----------------------------|:----------------|:----------|:--------|:----------------|:------|:-----------|:---------|:-------------|:--------------------|:----------------|:----------|:----------------------|:--------------|:---------------|:------------------|:------------|:-------|:--------|
| 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | | X | X | | | X | X | X | X | | X | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | |
| 2 | 7 |  |  |  |  |  | X | X | | X | X | | | X | X | | X | | X | | | | | | X | X | X | X | X | X | X | | | X | | X | | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/foch_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T21:42:54+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T22:02:48+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of foch/フォッシュ/福煦 (Azur Lane)
====================================
This is the dataset of foch/フォッシュ/福煦 (Azur Lane), containing 76 images and their tags.
The core tags of this character are 'breasts, purple\_hair, bangs, hair\_between\_eyes, multicolored\_hair, long\_hair, large\_breasts, ahoge, crossed\_bangs, grey\_hair, red\_eyes, pink\_eyes, blue\_hair, hair\_ornament, sidelocks', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
1416fda916724d8c218f585733f8b0bf02ea57b7 |
# Dataset of independence/インディペンデンス/独立 (Azur Lane)
This is the dataset of independence/インディペンデンス/独立 (Azur Lane), containing 68 images and their tags.
The core tags of this character are `breasts, red_eyes, bangs, long_hair, brown_hair, ahoge, hairband, large_breasts, hair_ornament, very_long_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 68 | 93.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/independence_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 68 | 56.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/independence_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 146 | 102.14 MiB | [Download](https://huggingface.co/datasets/CyberHarem/independence_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 68 | 86.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/independence_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 146 | 139.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/independence_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/independence_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 19 |  |  |  |  |  | blush, 1girl, looking_at_viewer, solo, white_shirt, blue_skirt, pleated_skirt, school_uniform, collared_shirt, smile, earrings, long_sleeves, black_pantyhose, cardigan, collarbone, jacket, white_background, closed_mouth, hairclip, official_alternate_costume, open_clothes, shoes, simple_background, striped_necktie |
| 1 | 14 |  |  |  |  |  | 1girl, looking_at_viewer, solo, bare_shoulders, red_gloves, sideboob, skirt, rudder_footwear, thighhighs, jacket, black_hair, rigging, bow_(weapon), flight_deck, headband, off_shoulder, sleeveless |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | blush | 1girl | looking_at_viewer | solo | white_shirt | blue_skirt | pleated_skirt | school_uniform | collared_shirt | smile | earrings | long_sleeves | black_pantyhose | cardigan | collarbone | jacket | white_background | closed_mouth | hairclip | official_alternate_costume | open_clothes | shoes | simple_background | striped_necktie | bare_shoulders | red_gloves | sideboob | skirt | rudder_footwear | thighhighs | black_hair | rigging | bow_(weapon) | flight_deck | headband | off_shoulder | sleeveless |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:--------------------|:-------|:--------------|:-------------|:----------------|:-----------------|:-----------------|:--------|:-----------|:---------------|:------------------|:-----------|:-------------|:---------|:-------------------|:---------------|:-----------|:-----------------------------|:---------------|:--------|:--------------------|:------------------|:-----------------|:-------------|:-----------|:--------|:------------------|:-------------|:-------------|:----------|:---------------|:--------------|:-----------|:---------------|:-------------|
| 0 | 19 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | |
| 1 | 14 |  |  |  |  |  | | X | X | X | | | | | | | | | | | | X | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/independence_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T21:42:59+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T22:01:19+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of independence/インディペンデンス/独立 (Azur Lane)
================================================
This is the dataset of independence/インディペンデンス/独立 (Azur Lane), containing 68 images and their tags.
The core tags of this character are 'breasts, red\_eyes, bangs, long\_hair, brown\_hair, ahoge, hairband, large\_breasts, hair\_ornament, very\_long\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
fffefea572ab55927cd35e39d0f9bfc175500cdf |
# Dataset of acasta/アカスタ/阿卡司塔 (Azur Lane)
This is the dataset of acasta/アカスタ/阿卡司塔 (Azur Lane), containing 24 images and their tags.
The core tags of this character are `black_hair, blue_eyes, bangs, breasts, short_hair, hat, multicolored_hair, blue_hair, bow, one_side_up, large_breasts, ribbon, beret, blue_bow, blunt_bangs, hair_bow, white_headwear`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 24 | 23.69 MiB | [Download](https://huggingface.co/datasets/CyberHarem/acasta_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 24 | 16.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/acasta_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 49 | 30.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/acasta_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 24 | 21.78 MiB | [Download](https://huggingface.co/datasets/CyberHarem/acasta_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 49 | 37.65 MiB | [Download](https://huggingface.co/datasets/CyberHarem/acasta_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/acasta_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 5 |  |  |  |  |  | 1girl, blush, looking_at_viewer, solo, white_shirt, black_skirt, closed_mouth, collared_shirt, long_sleeves, bag, blue_headwear, full_body, pleated_skirt, simple_background, black_choker, black_footwear, black_thighhighs, boots, chibi, coat, medium_hair, open_jacket, own_hands_together, shoes, sitting, smile, twitter_username, white_background |
| 1 | 6 |  |  |  |  |  | 1girl, blue_skirt, long_sleeves, simple_background, white_background, blush, looking_at_viewer, pleated_skirt, solo, white_thighhighs, frilled_skirt, cannon, garter_straps, high-waist_skirt, holding, loafers, machinery, medium_breasts, turret, white_shirt |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | looking_at_viewer | solo | white_shirt | black_skirt | closed_mouth | collared_shirt | long_sleeves | bag | blue_headwear | full_body | pleated_skirt | simple_background | black_choker | black_footwear | black_thighhighs | boots | chibi | coat | medium_hair | open_jacket | own_hands_together | shoes | sitting | smile | twitter_username | white_background | blue_skirt | white_thighhighs | frilled_skirt | cannon | garter_straps | high-waist_skirt | holding | loafers | machinery | medium_breasts | turret |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:--------------------|:-------|:--------------|:--------------|:---------------|:-----------------|:---------------|:------|:----------------|:------------|:----------------|:--------------------|:---------------|:-----------------|:-------------------|:--------|:--------|:-------|:--------------|:--------------|:---------------------|:--------|:----------|:--------|:-------------------|:-------------------|:-------------|:-------------------|:----------------|:---------|:----------------|:-------------------|:----------|:----------|:------------|:-----------------|:---------|
| 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | X | X | X | X | | | | X | | | | X | X | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/acasta_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T21:43:18+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T21:50:51+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of acasta/アカスタ/阿卡司塔 (Azur Lane)
=======================================
This is the dataset of acasta/アカスタ/阿卡司塔 (Azur Lane), containing 24 images and their tags.
The core tags of this character are 'black\_hair, blue\_eyes, bangs, breasts, short\_hair, hat, multicolored\_hair, blue\_hair, bow, one\_side\_up, large\_breasts, ribbon, beret, blue\_bow, blunt\_bangs, hair\_bow, white\_headwear', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
3ab4f21448349e6ec5c05674faa1e3ab3bf35796 |
# Dataset of spence/スペンス/斯彭斯 (Azur Lane)
This is the dataset of spence/スペンス/斯彭斯 (Azur Lane), containing 15 images and their tags.
The core tags of this character are `hair_ornament, long_hair, pink_hair, bangs, two_side_up, yellow_eyes, hat, beret`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 15 | 12.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/spence_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 15 | 8.55 MiB | [Download](https://huggingface.co/datasets/CyberHarem/spence_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 32 | 16.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/spence_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 15 | 11.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/spence_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 32 | 22.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/spence_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/spence_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 15 |  |  |  |  |  | hair_bobbles, blush, 1girl, tears, open_mouth, sleeveless, dress, simple_background, solo, hat_feather, looking_at_viewer, white_background, black_pantyhose, sailor_collar, smile |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | hair_bobbles | blush | 1girl | tears | open_mouth | sleeveless | dress | simple_background | solo | hat_feather | looking_at_viewer | white_background | black_pantyhose | sailor_collar | smile |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------|:--------|:--------|:--------|:-------------|:-------------|:--------|:--------------------|:-------|:--------------|:--------------------|:-------------------|:------------------|:----------------|:--------|
| 0 | 15 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/spence_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T21:43:21+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T21:48:17+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of spence/スペンス/斯彭斯 (Azur Lane)
======================================
This is the dataset of spence/スペンス/斯彭斯 (Azur Lane), containing 15 images and their tags.
The core tags of this character are 'hair\_ornament, long\_hair, pink\_hair, bangs, two\_side\_up, yellow\_eyes, hat, beret', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
060ad3df88a6ba5f5546c622652290f38e73ceba |
#### Attention: This dataset is a summary and reformat pulled from github code.
You should make your own assumptions based on this.
In fact, there is another dataset I formed through parsing that addresses several points:
- out of 500k python related items, most of them are python-ish, not pythonic
- the majority of the items here contain excessive licensing inclusion of original code
- the items here are sometimes not even python but have references
- There's a whole lot of gpl summaries floating on the code responses or instructions
As such, you are probably not getting good data to begin with, but this should be used as a starting point at best.
You have been warned.
| jtatman/python-code-dataset-500k | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"license:mit",
"instructional",
"python",
"code",
"region:us"
] | 2024-01-13T21:44:31+00:00 | {"license": "mit", "size_categories": ["100K<n<1M"], "task_categories": ["text-generation"], "pretty_name": "github_python", "dataset_info": {"features": [{"name": "output", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "system", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 922266591, "num_examples": 559515}], "download_size": 346944286, "dataset_size": 922266591}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "tags": ["instructional", "python", "code"]} | 2024-01-23T21:39:13+00:00 | [] | [] | TAGS
#task_categories-text-generation #size_categories-100K<n<1M #license-mit #instructional #python #code #region-us
|
#### Attention: This dataset is a summary and reformat pulled from github code.
You should make your own assumptions based on this.
In fact, there is another dataset I formed through parsing that addresses several points:
- out of 500k python related items, most of them are python-ish, not pythonic
- the majority of the items here contain excessive licensing inclusion of original code
- the items here are sometimes not even python but have references
- There's a whole lot of gpl summaries floating on the code responses or instructions
As such, you are probably not getting good data to begin with, but this should be used as a starting point at best.
You have been warned.
| [
"#### Attention: This dataset is a summary and reformat pulled from github code. \nYou should make your own assumptions based on this.\nIn fact, there is another dataset I formed through parsing that addresses several points:\n- out of 500k python related items, most of them are python-ish, not pythonic\n- the majority of the items here contain excessive licensing inclusion of original code\n- the items here are sometimes not even python but have references\n- There's a whole lot of gpl summaries floating on the code responses or instructions\n\nAs such, you are probably not getting good data to begin with, but this should be used as a starting point at best.\nYou have been warned."
] | [
"TAGS\n#task_categories-text-generation #size_categories-100K<n<1M #license-mit #instructional #python #code #region-us \n",
"#### Attention: This dataset is a summary and reformat pulled from github code. \nYou should make your own assumptions based on this.\nIn fact, there is another dataset I formed through parsing that addresses several points:\n- out of 500k python related items, most of them are python-ish, not pythonic\n- the majority of the items here contain excessive licensing inclusion of original code\n- the items here are sometimes not even python but have references\n- There's a whole lot of gpl summaries floating on the code responses or instructions\n\nAs such, you are probably not getting good data to begin with, but this should be used as a starting point at best.\nYou have been warned."
] |
337131bee7d9e3a6fb4bb6b9c6eb8fcfb55c6575 |
# Dataset of am_ksg/AmKSG/KSG (Girls' Frontline)
This is the dataset of am_ksg/AmKSG/KSG (Girls' Frontline), containing 12 images and their tags.
The core tags of this character are `bangs, short_hair, sunglasses, white_hair, grey_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 12 | 15.85 MiB | [Download](https://huggingface.co/datasets/CyberHarem/am_ksg_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 12 | 7.90 MiB | [Download](https://huggingface.co/datasets/CyberHarem/am_ksg_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 25 | 15.12 MiB | [Download](https://huggingface.co/datasets/CyberHarem/am_ksg_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 12 | 13.40 MiB | [Download](https://huggingface.co/datasets/CyberHarem/am_ksg_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 25 | 23.94 MiB | [Download](https://huggingface.co/datasets/CyberHarem/am_ksg_girlsfrontline/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/am_ksg_girlsfrontline',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------|
| 0 | 12 |  |  |  |  |  | 1girl, jacket, solo, fingerless_gloves, hood_up, gun, looking_at_viewer |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | jacket | solo | fingerless_gloves | hood_up | gun | looking_at_viewer |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------|:-------|:--------------------|:----------|:------|:--------------------|
| 0 | 12 |  |  |  |  |  | X | X | X | X | X | X | X |
| CyberHarem/am_ksg_girlsfrontline | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T21:45:04+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T21:50:13+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of am\_ksg/AmKSG/KSG (Girls' Frontline)
===============================================
This is the dataset of am\_ksg/AmKSG/KSG (Girls' Frontline), containing 12 images and their tags.
The core tags of this character are 'bangs, short\_hair, sunglasses, white\_hair, grey\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
94812c6b213b2363534e7774acb4b8f3b45c5ac4 |
# Dataset of p38/P38/P38 (Girls' Frontline)
This is the dataset of p38/P38/P38 (Girls' Frontline), containing 11 images and their tags.
The core tags of this character are `brown_hair, hat, garrison_cap, military_hat, long_hair, purple_eyes, bangs`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:--------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 11 | 6.75 MiB | [Download](https://huggingface.co/datasets/CyberHarem/p38_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 11 | 5.59 MiB | [Download](https://huggingface.co/datasets/CyberHarem/p38_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 21 | 10.99 MiB | [Download](https://huggingface.co/datasets/CyberHarem/p38_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 11 | 6.48 MiB | [Download](https://huggingface.co/datasets/CyberHarem/p38_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 21 | 12.02 MiB | [Download](https://huggingface.co/datasets/CyberHarem/p38_girlsfrontline/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/p38_girlsfrontline',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 11 |  |  |  |  |  | 1girl, military_uniform, solo, belt, white_background, handgun, iron_cross, jacket, open_mouth, black_skirt, boots, holding_gun, holster, looking_at_viewer, simple_background, thighhighs, collared_shirt, pleated_skirt, pouch, walther |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | military_uniform | solo | belt | white_background | handgun | iron_cross | jacket | open_mouth | black_skirt | boots | holding_gun | holster | looking_at_viewer | simple_background | thighhighs | collared_shirt | pleated_skirt | pouch | walther |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------------------|:-------|:-------|:-------------------|:----------|:-------------|:---------|:-------------|:--------------|:--------|:--------------|:----------|:--------------------|:--------------------|:-------------|:-----------------|:----------------|:--------|:----------|
| 0 | 11 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/p38_girlsfrontline | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T21:45:07+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T21:49:00+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of p38/P38/P38 (Girls' Frontline)
=========================================
This is the dataset of p38/P38/P38 (Girls' Frontline), containing 11 images and their tags.
The core tags of this character are 'brown\_hair, hat, garrison\_cap, military\_hat, long\_hair, purple\_eyes, bangs', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
2c04d53791dec5a70c2cabdb30a248f013549867 |
An augmented and further modified version of [LimaRP](https://huggingface.co/datasets/lemonilia/LimaRP) in Fastchat format, modified in the following ways:
- The first prompt is modified to add context and simple references to aspects of the conversation (OOC, use of emojis, content), include persona descriptions of the characters involved, scenario descriptions and content tags.
- Certain irrelevant tags removed from first prompt (4K, grammarchecked, etc.)
- Any placeholders replaced by randomly generated names from [Faker](https://pypi.org/project/Faker/), with proper introductions introduced in the first prompt.
- All split conversations were joined to train long-context models (you may need to re-split them to fit in context length if you are not doing this).
- The assistant never plays multiple characters and always plays only a single character consistently. The user may play multiple characters, and if this is the case, it is clearly explained in the first prompt. | grimulkan/LimaRP-augmented | [
"license:unknown",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T21:46:03+00:00 | {"license": "unknown", "tags": ["not-for-all-audiences"]} | 2024-01-24T00:01:23+00:00 | [] | [] | TAGS
#license-unknown #not-for-all-audiences #region-us
|
An augmented and further modified version of LimaRP in Fastchat format, modified in the following ways:
- The first prompt is modified to add context and simple references to aspects of the conversation (OOC, use of emojis, content), include persona descriptions of the characters involved, scenario descriptions and content tags.
- Certain irrelevant tags removed from first prompt (4K, grammarchecked, etc.)
- Any placeholders replaced by randomly generated names from Faker, with proper introductions introduced in the first prompt.
- All split conversations were joined to train long-context models (you may need to re-split them to fit in context length if you are not doing this).
- The assistant never plays multiple characters and always plays only a single character consistently. The user may play multiple characters, and if this is the case, it is clearly explained in the first prompt. | [] | [
"TAGS\n#license-unknown #not-for-all-audiences #region-us \n"
] |
fdf052d6709f428a80ad59e5bf006f2127ed0bf3 |
An augmented and further modified version of [Jannie-log](https://huggingface.co/datasets/v2ray/jannie-log) moxxie proxy logs in Fastchat format, modified in the following ways:
- The first prompt is modified to add context and simple references to aspects of the conversation (OOC, use of emojis, content).
- Any placeholders replaced by randomly generated names from [Faker](https://pypi.org/project/Faker/), with proper introductions introduced in the first prompt.
- All split conversations were joined to train long-context models (you may need to re-split them to fit in context length if you are not doing this) - this is the main reason you'd want to use this version of the dataset.
- Non-multiround conversations removed.
- Only English-language output is included.
- OpenAI, Anthropic, etc. refusals and moralizing statements removed. Proxy errors removed.
- Repeated requests by the user to ignore alignment are removed. You no longer need this if you are fine-tuning an uncensored base model (and they reduce the quality of the training).
- Proxy logs include lots of repeated conversations that go down different paths. All of these duplicates have been removed, keeping the longest unique path through the conversation tree.
- **Only GPT-4 output is included**. | grimulkan/jannie-log-augmented | [
"license:unknown",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T21:57:24+00:00 | {"license": "unknown", "tags": ["not-for-all-audiences"]} | 2024-01-24T00:01:12+00:00 | [] | [] | TAGS
#license-unknown #not-for-all-audiences #region-us
|
An augmented and further modified version of Jannie-log moxxie proxy logs in Fastchat format, modified in the following ways:
- The first prompt is modified to add context and simple references to aspects of the conversation (OOC, use of emojis, content).
- Any placeholders replaced by randomly generated names from Faker, with proper introductions introduced in the first prompt.
- All split conversations were joined to train long-context models (you may need to re-split them to fit in context length if you are not doing this) - this is the main reason you'd want to use this version of the dataset.
- Non-multiround conversations removed.
- Only English-language output is included.
- OpenAI, Anthropic, etc. refusals and moralizing statements removed. Proxy errors removed.
- Repeated requests by the user to ignore alignment are removed. You no longer need this if you are fine-tuning an uncensored base model (and they reduce the quality of the training).
- Proxy logs include lots of repeated conversations that go down different paths. All of these duplicates have been removed, keeping the longest unique path through the conversation tree.
- Only GPT-4 output is included. | [] | [
"TAGS\n#license-unknown #not-for-all-audiences #region-us \n"
] |
d2e4deb2a604e1ce7258ee3683676b3650d95e2a |
# Dataset Card for Evaluation run of CultriX/MistralTrix-SLERP
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [CultriX/MistralTrix-SLERP](https://huggingface.co/CultriX/MistralTrix-SLERP) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_CultriX__MistralTrix-SLERP",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T21:57:09.526776](https://huggingface.co/datasets/open-llm-leaderboard/details_CultriX__MistralTrix-SLERP/blob/main/results_2024-01-13T21-57-09.526776.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6548762565479429,
"acc_stderr": 0.03203510482454222,
"acc_norm": 0.65460268949256,
"acc_norm_stderr": 0.0326982976348309,
"mc1": 0.49571603427172584,
"mc1_stderr": 0.017502858577371275,
"mc2": 0.653460703870151,
"mc2_stderr": 0.015284820606060751
},
"harness|arc:challenge|25": {
"acc": 0.6843003412969283,
"acc_stderr": 0.013582571095815291,
"acc_norm": 0.7081911262798635,
"acc_norm_stderr": 0.013284525292403513
},
"harness|hellaswag|10": {
"acc": 0.6971718781119299,
"acc_stderr": 0.0045854245130121036,
"acc_norm": 0.8754232224656443,
"acc_norm_stderr": 0.0032956349076664645
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252605,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252605
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6592592592592592,
"acc_stderr": 0.04094376269996792,
"acc_norm": 0.6592592592592592,
"acc_norm_stderr": 0.04094376269996792
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6842105263157895,
"acc_stderr": 0.0378272898086547,
"acc_norm": 0.6842105263157895,
"acc_norm_stderr": 0.0378272898086547
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.65,
"acc_stderr": 0.0479372485441102,
"acc_norm": 0.65,
"acc_norm_stderr": 0.0479372485441102
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7169811320754716,
"acc_stderr": 0.027724236492700914,
"acc_norm": 0.7169811320754716,
"acc_norm_stderr": 0.027724236492700914
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.03476590104304134,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.03476590104304134
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.47,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.47,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.53,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.53,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6589595375722543,
"acc_stderr": 0.03614665424180826,
"acc_norm": 0.6589595375722543,
"acc_norm_stderr": 0.03614665424180826
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4411764705882353,
"acc_stderr": 0.049406356306056595,
"acc_norm": 0.4411764705882353,
"acc_norm_stderr": 0.049406356306056595
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.76,
"acc_stderr": 0.04292346959909283,
"acc_norm": 0.76,
"acc_norm_stderr": 0.04292346959909283
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5957446808510638,
"acc_stderr": 0.03208115750788684,
"acc_norm": 0.5957446808510638,
"acc_norm_stderr": 0.03208115750788684
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5,
"acc_stderr": 0.047036043419179864,
"acc_norm": 0.5,
"acc_norm_stderr": 0.047036043419179864
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5310344827586206,
"acc_stderr": 0.04158632762097828,
"acc_norm": 0.5310344827586206,
"acc_norm_stderr": 0.04158632762097828
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.42592592592592593,
"acc_stderr": 0.02546714904546955,
"acc_norm": 0.42592592592592593,
"acc_norm_stderr": 0.02546714904546955
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.46825396825396826,
"acc_stderr": 0.04463112720677172,
"acc_norm": 0.46825396825396826,
"acc_norm_stderr": 0.04463112720677172
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.35,
"acc_stderr": 0.047937248544110196,
"acc_norm": 0.35,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7870967741935484,
"acc_stderr": 0.023287665127268542,
"acc_norm": 0.7870967741935484,
"acc_norm_stderr": 0.023287665127268542
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5024630541871922,
"acc_stderr": 0.035179450386910616,
"acc_norm": 0.5024630541871922,
"acc_norm_stderr": 0.035179450386910616
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.7,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.793939393939394,
"acc_stderr": 0.0315841532404771,
"acc_norm": 0.793939393939394,
"acc_norm_stderr": 0.0315841532404771
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7828282828282829,
"acc_stderr": 0.029376616484945633,
"acc_norm": 0.7828282828282829,
"acc_norm_stderr": 0.029376616484945633
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9015544041450777,
"acc_stderr": 0.02150024957603348,
"acc_norm": 0.9015544041450777,
"acc_norm_stderr": 0.02150024957603348
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6666666666666666,
"acc_stderr": 0.023901157979402534,
"acc_norm": 0.6666666666666666,
"acc_norm_stderr": 0.023901157979402534
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3592592592592593,
"acc_stderr": 0.029252905927251972,
"acc_norm": 0.3592592592592593,
"acc_norm_stderr": 0.029252905927251972
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.680672268907563,
"acc_stderr": 0.030283995525884396,
"acc_norm": 0.680672268907563,
"acc_norm_stderr": 0.030283995525884396
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3509933774834437,
"acc_stderr": 0.03896981964257375,
"acc_norm": 0.3509933774834437,
"acc_norm_stderr": 0.03896981964257375
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8532110091743119,
"acc_stderr": 0.01517314184512625,
"acc_norm": 0.8532110091743119,
"acc_norm_stderr": 0.01517314184512625
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.49537037037037035,
"acc_stderr": 0.03409825519163572,
"acc_norm": 0.49537037037037035,
"acc_norm_stderr": 0.03409825519163572
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8333333333333334,
"acc_stderr": 0.026156867523931045,
"acc_norm": 0.8333333333333334,
"acc_norm_stderr": 0.026156867523931045
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7974683544303798,
"acc_stderr": 0.026160568246601443,
"acc_norm": 0.7974683544303798,
"acc_norm_stderr": 0.026160568246601443
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6905829596412556,
"acc_stderr": 0.03102441174057221,
"acc_norm": 0.6905829596412556,
"acc_norm_stderr": 0.03102441174057221
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.816793893129771,
"acc_stderr": 0.03392770926494733,
"acc_norm": 0.816793893129771,
"acc_norm_stderr": 0.03392770926494733
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7933884297520661,
"acc_stderr": 0.03695980128098824,
"acc_norm": 0.7933884297520661,
"acc_norm_stderr": 0.03695980128098824
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.0401910747255735,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.0401910747255735
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.754601226993865,
"acc_stderr": 0.03380939813943354,
"acc_norm": 0.754601226993865,
"acc_norm_stderr": 0.03380939813943354
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.4375,
"acc_stderr": 0.04708567521880525,
"acc_norm": 0.4375,
"acc_norm_stderr": 0.04708567521880525
},
"harness|hendrycksTest-management|5": {
"acc": 0.7766990291262136,
"acc_stderr": 0.04123553189891431,
"acc_norm": 0.7766990291262136,
"acc_norm_stderr": 0.04123553189891431
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8888888888888888,
"acc_stderr": 0.020588491316092375,
"acc_norm": 0.8888888888888888,
"acc_norm_stderr": 0.020588491316092375
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.72,
"acc_stderr": 0.045126085985421276,
"acc_norm": 0.72,
"acc_norm_stderr": 0.045126085985421276
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8326947637292464,
"acc_stderr": 0.013347327202920332,
"acc_norm": 0.8326947637292464,
"acc_norm_stderr": 0.013347327202920332
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7456647398843931,
"acc_stderr": 0.023445826276545543,
"acc_norm": 0.7456647398843931,
"acc_norm_stderr": 0.023445826276545543
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.4402234636871508,
"acc_stderr": 0.016602564615049935,
"acc_norm": 0.4402234636871508,
"acc_norm_stderr": 0.016602564615049935
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7320261437908496,
"acc_stderr": 0.025360603796242557,
"acc_norm": 0.7320261437908496,
"acc_norm_stderr": 0.025360603796242557
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7106109324758842,
"acc_stderr": 0.025755865922632945,
"acc_norm": 0.7106109324758842,
"acc_norm_stderr": 0.025755865922632945
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7623456790123457,
"acc_stderr": 0.02368359183700856,
"acc_norm": 0.7623456790123457,
"acc_norm_stderr": 0.02368359183700856
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.4929078014184397,
"acc_stderr": 0.02982449855912901,
"acc_norm": 0.4929078014184397,
"acc_norm_stderr": 0.02982449855912901
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.46088657105606257,
"acc_stderr": 0.012731102790504515,
"acc_norm": 0.46088657105606257,
"acc_norm_stderr": 0.012731102790504515
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6654411764705882,
"acc_stderr": 0.028661996202335303,
"acc_norm": 0.6654411764705882,
"acc_norm_stderr": 0.028661996202335303
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6830065359477124,
"acc_stderr": 0.018824219512706207,
"acc_norm": 0.6830065359477124,
"acc_norm_stderr": 0.018824219512706207
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6727272727272727,
"acc_stderr": 0.0449429086625209,
"acc_norm": 0.6727272727272727,
"acc_norm_stderr": 0.0449429086625209
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7387755102040816,
"acc_stderr": 0.028123429335142777,
"acc_norm": 0.7387755102040816,
"acc_norm_stderr": 0.028123429335142777
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.845771144278607,
"acc_stderr": 0.025538433368578337,
"acc_norm": 0.845771144278607,
"acc_norm_stderr": 0.025538433368578337
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.84,
"acc_stderr": 0.03684529491774708,
"acc_norm": 0.84,
"acc_norm_stderr": 0.03684529491774708
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5481927710843374,
"acc_stderr": 0.03874371556587953,
"acc_norm": 0.5481927710843374,
"acc_norm_stderr": 0.03874371556587953
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8362573099415205,
"acc_stderr": 0.028380919596145866,
"acc_norm": 0.8362573099415205,
"acc_norm_stderr": 0.028380919596145866
},
"harness|truthfulqa:mc|0": {
"mc1": 0.49571603427172584,
"mc1_stderr": 0.017502858577371275,
"mc2": 0.653460703870151,
"mc2_stderr": 0.015284820606060751
},
"harness|winogrande|5": {
"acc": 0.8168902920284136,
"acc_stderr": 0.01086977863316837
},
"harness|gsm8k|5": {
"acc": 0.711144806671721,
"acc_stderr": 0.012484219800126666
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
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[More Information Needed] | open-llm-leaderboard/details_CultriX__MistralTrix-SLERP | [
"region:us"
] | 2024-01-13T21:59:25+00:00 | {"pretty_name": "Evaluation run of CultriX/MistralTrix-SLERP", "dataset_summary": "Dataset automatically created during the evaluation run of model [CultriX/MistralTrix-SLERP](https://huggingface.co/CultriX/MistralTrix-SLERP) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_CultriX__MistralTrix-SLERP\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T21:57:09.526776](https://huggingface.co/datasets/open-llm-leaderboard/details_CultriX__MistralTrix-SLERP/blob/main/results_2024-01-13T21-57-09.526776.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6548762565479429,\n \"acc_stderr\": 0.03203510482454222,\n \"acc_norm\": 0.65460268949256,\n \"acc_norm_stderr\": 0.0326982976348309,\n \"mc1\": 0.49571603427172584,\n \"mc1_stderr\": 0.017502858577371275,\n \"mc2\": 0.653460703870151,\n \"mc2_stderr\": 0.015284820606060751\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6843003412969283,\n \"acc_stderr\": 0.013582571095815291,\n \"acc_norm\": 0.7081911262798635,\n \"acc_norm_stderr\": 0.013284525292403513\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6971718781119299,\n \"acc_stderr\": 0.0045854245130121036,\n \"acc_norm\": 0.8754232224656443,\n \"acc_norm_stderr\": 0.0032956349076664645\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6592592592592592,\n \"acc_stderr\": 0.04094376269996792,\n \"acc_norm\": 0.6592592592592592,\n \"acc_norm_stderr\": 0.04094376269996792\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6842105263157895,\n \"acc_stderr\": 0.0378272898086547,\n \"acc_norm\": 0.6842105263157895,\n \"acc_norm_stderr\": 0.0378272898086547\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.65,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.7169811320754716,\n \"acc_stderr\": 0.027724236492700914,\n \"acc_norm\": 0.7169811320754716,\n \"acc_norm_stderr\": 0.027724236492700914\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6589595375722543,\n \"acc_stderr\": 0.03614665424180826,\n \"acc_norm\": 0.6589595375722543,\n \"acc_norm_stderr\": 0.03614665424180826\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.049406356306056595,\n \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.049406356306056595\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.76,\n \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5957446808510638,\n \"acc_stderr\": 0.03208115750788684,\n \"acc_norm\": 0.5957446808510638,\n \"acc_norm_stderr\": 0.03208115750788684\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.047036043419179864,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.047036043419179864\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5310344827586206,\n \"acc_stderr\": 0.04158632762097828,\n \"acc_norm\": 0.5310344827586206,\n \"acc_norm_stderr\": 0.04158632762097828\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.42592592592592593,\n \"acc_stderr\": 0.02546714904546955,\n \"acc_norm\": 0.42592592592592593,\n \"acc_norm_stderr\": 0.02546714904546955\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.46825396825396826,\n \"acc_stderr\": 0.04463112720677172,\n \"acc_norm\": 0.46825396825396826,\n \"acc_norm_stderr\": 0.04463112720677172\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7870967741935484,\n \"acc_stderr\": 0.023287665127268542,\n \"acc_norm\": 0.7870967741935484,\n \"acc_norm_stderr\": 0.023287665127268542\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5024630541871922,\n \"acc_stderr\": 0.035179450386910616,\n \"acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.035179450386910616\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.793939393939394,\n \"acc_stderr\": 0.0315841532404771,\n \"acc_norm\": 0.793939393939394,\n \"acc_norm_stderr\": 0.0315841532404771\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7828282828282829,\n \"acc_stderr\": 0.029376616484945633,\n \"acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.029376616484945633\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.02150024957603348,\n \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.02150024957603348\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.023901157979402534,\n \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.023901157979402534\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.3592592592592593,\n \"acc_stderr\": 0.029252905927251972,\n \"acc_norm\": 0.3592592592592593,\n \"acc_norm_stderr\": 0.029252905927251972\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.680672268907563,\n \"acc_stderr\": 0.030283995525884396,\n \"acc_norm\": 0.680672268907563,\n \"acc_norm_stderr\": 0.030283995525884396\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.3509933774834437,\n \"acc_stderr\": 0.03896981964257375,\n \"acc_norm\": 0.3509933774834437,\n \"acc_norm_stderr\": 0.03896981964257375\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8532110091743119,\n \"acc_stderr\": 0.01517314184512625,\n \"acc_norm\": 0.8532110091743119,\n \"acc_norm_stderr\": 0.01517314184512625\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.49537037037037035,\n \"acc_stderr\": 0.03409825519163572,\n \"acc_norm\": 0.49537037037037035,\n \"acc_norm_stderr\": 0.03409825519163572\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8333333333333334,\n \"acc_stderr\": 0.026156867523931045,\n \"acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.026156867523931045\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7974683544303798,\n \"acc_stderr\": 0.026160568246601443,\n \"acc_norm\": 0.7974683544303798,\n \"acc_norm_stderr\": 0.026160568246601443\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.816793893129771,\n \"acc_stderr\": 0.03392770926494733,\n \"acc_norm\": 0.816793893129771,\n \"acc_norm_stderr\": 0.03392770926494733\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.0401910747255735,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.0401910747255735\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.754601226993865,\n \"acc_stderr\": 0.03380939813943354,\n \"acc_norm\": 0.754601226993865,\n \"acc_norm_stderr\": 0.03380939813943354\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4375,\n \"acc_stderr\": 0.04708567521880525,\n \"acc_norm\": 0.4375,\n \"acc_norm_stderr\": 0.04708567521880525\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8888888888888888,\n \"acc_stderr\": 0.020588491316092375,\n \"acc_norm\": 0.8888888888888888,\n \"acc_norm_stderr\": 0.020588491316092375\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.72,\n \"acc_stderr\": 0.045126085985421276,\n \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.045126085985421276\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8326947637292464,\n \"acc_stderr\": 0.013347327202920332,\n \"acc_norm\": 0.8326947637292464,\n \"acc_norm_stderr\": 0.013347327202920332\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7456647398843931,\n \"acc_stderr\": 0.023445826276545543,\n \"acc_norm\": 0.7456647398843931,\n \"acc_norm_stderr\": 0.023445826276545543\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4402234636871508,\n \"acc_stderr\": 0.016602564615049935,\n \"acc_norm\": 0.4402234636871508,\n \"acc_norm_stderr\": 0.016602564615049935\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.7320261437908496,\n \"acc_stderr\": 0.025360603796242557,\n \"acc_norm\": 0.7320261437908496,\n \"acc_norm_stderr\": 0.025360603796242557\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7106109324758842,\n \"acc_stderr\": 0.025755865922632945,\n \"acc_norm\": 0.7106109324758842,\n \"acc_norm_stderr\": 0.025755865922632945\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.7623456790123457,\n \"acc_stderr\": 0.02368359183700856,\n \"acc_norm\": 0.7623456790123457,\n \"acc_norm_stderr\": 0.02368359183700856\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \"acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.46088657105606257,\n \"acc_stderr\": 0.012731102790504515,\n \"acc_norm\": 0.46088657105606257,\n \"acc_norm_stderr\": 0.012731102790504515\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.6654411764705882,\n \"acc_stderr\": 0.028661996202335303,\n \"acc_norm\": 0.6654411764705882,\n \"acc_norm_stderr\": 0.028661996202335303\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.6830065359477124,\n \"acc_stderr\": 0.018824219512706207,\n \"acc_norm\": 0.6830065359477124,\n \"acc_norm_stderr\": 0.018824219512706207\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.7387755102040816,\n \"acc_stderr\": 0.028123429335142777,\n \"acc_norm\": 0.7387755102040816,\n \"acc_norm_stderr\": 0.028123429335142777\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.845771144278607,\n \"acc_stderr\": 0.025538433368578337,\n \"acc_norm\": 0.845771144278607,\n \"acc_norm_stderr\": 0.025538433368578337\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774708,\n \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774708\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5481927710843374,\n \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.49571603427172584,\n \"mc1_stderr\": 0.017502858577371275,\n \"mc2\": 0.653460703870151,\n \"mc2_stderr\": 0.015284820606060751\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.8168902920284136,\n \"acc_stderr\": 0.01086977863316837\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.711144806671721,\n \"acc_stderr\": 0.012484219800126666\n }\n}\n```", "repo_url": "https://huggingface.co/CultriX/MistralTrix-SLERP", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2024_01_13T21_57_09.526776", "path": ["**/details_harness|arc:challenge|25_2024-01-13T21-57-09.526776.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-01-13T21-57-09.526776.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_01_13T21_57_09.526776", "path": ["**/details_harness|gsm8k|5_2024-01-13T21-57-09.526776.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-01-13T21-57-09.526776.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_01_13T21_57_09.526776", "path": ["**/details_harness|hellaswag|10_2024-01-13T21-57-09.526776.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-01-13T21-57-09.526776.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_01_13T21_57_09.526776", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T21-57-09.526776.parquet", 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"path": ["**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-13T21-57-09.526776.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_microeconomics_5", "data_files": [{"split": "2024_01_13T21_57_09.526776", "path": ["**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-13T21-57-09.526776.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-13T21-57-09.526776.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_physics_5", "data_files": [{"split": "2024_01_13T21_57_09.526776", "path": ["**/details_harness|hendrycksTest-high_school_physics|5_2024-01-13T21-57-09.526776.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_physics|5_2024-01-13T21-57-09.526776.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_psychology_5", "data_files": [{"split": "2024_01_13T21_57_09.526776", "path": 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"harness_hendrycksTest_high_school_world_history_5", "data_files": [{"split": "2024_01_13T21_57_09.526776", "path": ["**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T21-57-09.526776.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T21-57-09.526776.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_aging_5", "data_files": [{"split": "2024_01_13T21_57_09.526776", "path": ["**/details_harness|hendrycksTest-human_aging|5_2024-01-13T21-57-09.526776.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-human_aging|5_2024-01-13T21-57-09.526776.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_sexuality_5", "data_files": [{"split": "2024_01_13T21_57_09.526776", "path": ["**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T21-57-09.526776.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T21-57-09.526776.parquet"]}]}, {"config_name": "harness_hendrycksTest_international_law_5", "data_files": [{"split": "2024_01_13T21_57_09.526776", "path": ["**/details_harness|hendrycksTest-international_law|5_2024-01-13T21-57-09.526776.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-international_law|5_2024-01-13T21-57-09.526776.parquet"]}]}, {"config_name": "harness_hendrycksTest_jurisprudence_5", "data_files": [{"split": "2024_01_13T21_57_09.526776", "path": ["**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T21-57-09.526776.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T21-57-09.526776.parquet"]}]}, {"config_name": "harness_hendrycksTest_logical_fallacies_5", "data_files": [{"split": "2024_01_13T21_57_09.526776", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T21-57-09.526776.parquet"]}, {"split": 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"path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-13T21-57-09.526776.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2024_01_13T21_57_09.526776", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T21-57-09.526776.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T21-57-09.526776.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2024_01_13T21_57_09.526776", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T21-57-09.526776.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T21-57-09.526776.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2024_01_13T21_57_09.526776", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T21-57-09.526776.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T21-57-09.526776.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2024_01_13T21_57_09.526776", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T21-57-09.526776.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T21-57-09.526776.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2024_01_13T21_57_09.526776", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T21-57-09.526776.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T21-57-09.526776.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2024_01_13T21_57_09.526776", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T21-57-09.526776.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T21-57-09.526776.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2024_01_13T21_57_09.526776", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T21-57-09.526776.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T21-57-09.526776.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2024_01_13T21_57_09.526776", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T21-57-09.526776.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T21-57-09.526776.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2024_01_13T21_57_09.526776", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T21-57-09.526776.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T21-57-09.526776.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_01_13T21_57_09.526776", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T21-57-09.526776.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T21-57-09.526776.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_01_13T21_57_09.526776", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T21-57-09.526776.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T21-57-09.526776.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_01_13T21_57_09.526776", "path": 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["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T21-57-09.526776.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T21-57-09.526776.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_13T21_57_09.526776", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T21-57-09.526776.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T21-57-09.526776.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_13T21_57_09.526776", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T21-57-09.526776.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T21-57-09.526776.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_13T21_57_09.526776", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T21-57-09.526776.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T21-57-09.526776.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T21_57_09.526776", "path": ["**/details_harness|winogrande|5_2024-01-13T21-57-09.526776.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T21-57-09.526776.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T21_57_09.526776", "path": ["results_2024-01-13T21-57-09.526776.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T21-57-09.526776.parquet"]}]}]} | 2024-01-13T21:59:45+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of CultriX/MistralTrix-SLERP
Dataset automatically created during the evaluation run of model CultriX/MistralTrix-SLERP on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-13T21:57:09.526776(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of CultriX/MistralTrix-SLERP\n\n\n\nDataset automatically created during the evaluation run of model CultriX/MistralTrix-SLERP on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T21:57:09.526776(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of CultriX/MistralTrix-SLERP\n\n\n\nDataset automatically created during the evaluation run of model CultriX/MistralTrix-SLERP on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T21:57:09.526776(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
a7532bce5f54ad9ee318b789092c26f8a458fdd0 |
# Dataset Card for Evaluation run of kevin009/flyingllama
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [kevin009/flyingllama](https://huggingface.co/kevin009/flyingllama) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_kevin009__flyingllama",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T22:02:33.000952](https://huggingface.co/datasets/open-llm-leaderboard/details_kevin009__flyingllama/blob/main/results_2024-01-13T22-02-33.000952.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.26132503313334,
"acc_stderr": 0.030889306167362122,
"acc_norm": 0.26324892209928613,
"acc_norm_stderr": 0.03171228882658279,
"mc1": 0.23990208078335373,
"mc1_stderr": 0.014948812679062133,
"mc2": 0.41600624216438986,
"mc2_stderr": 0.01490081265282921
},
"harness|arc:challenge|25": {
"acc": 0.21245733788395904,
"acc_stderr": 0.011953482906582949,
"acc_norm": 0.24744027303754265,
"acc_norm_stderr": 0.01261035266329267
},
"harness|hellaswag|10": {
"acc": 0.32642899820752835,
"acc_stderr": 0.004679479763516778,
"acc_norm": 0.38348934475204144,
"acc_norm_stderr": 0.004852420856631477
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.19,
"acc_stderr": 0.039427724440366234,
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}
```
## Dataset Details
### Dataset Description
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### Direct Use
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## Dataset Structure
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## Dataset Creation
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### Source Data
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#### Data Collection and Processing
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[More Information Needed] | open-llm-leaderboard/details_kevin009__flyingllama | [
"region:us"
] | 2024-01-13T22:03:52+00:00 | {"pretty_name": "Evaluation run of kevin009/flyingllama", "dataset_summary": "Dataset automatically created during the evaluation run of model [kevin009/flyingllama](https://huggingface.co/kevin009/flyingllama) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_kevin009__flyingllama\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T22:02:33.000952](https://huggingface.co/datasets/open-llm-leaderboard/details_kevin009__flyingllama/blob/main/results_2024-01-13T22-02-33.000952.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.26132503313334,\n \"acc_stderr\": 0.030889306167362122,\n \"acc_norm\": 0.26324892209928613,\n \"acc_norm_stderr\": 0.03171228882658279,\n \"mc1\": 0.23990208078335373,\n \"mc1_stderr\": 0.014948812679062133,\n \"mc2\": 0.41600624216438986,\n \"mc2_stderr\": 0.01490081265282921\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.21245733788395904,\n \"acc_stderr\": 0.011953482906582949,\n \"acc_norm\": 0.24744027303754265,\n \"acc_norm_stderr\": 0.01261035266329267\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.32642899820752835,\n \"acc_stderr\": 0.004679479763516778,\n \"acc_norm\": 0.38348934475204144,\n \"acc_norm_stderr\": 0.004852420856631477\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.19,\n \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\": 0.19,\n \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness|hendrycksTest-anatomy|5\": {\n 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"latest", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T22-02-33.000952.parquet"]}]}, {"config_name": "harness_hendrycksTest_machine_learning_5", "data_files": [{"split": "2024_01_13T22_02_33.000952", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T22-02-33.000952.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T22-02-33.000952.parquet"]}]}, {"config_name": "harness_hendrycksTest_management_5", "data_files": [{"split": "2024_01_13T22_02_33.000952", "path": ["**/details_harness|hendrycksTest-management|5_2024-01-13T22-02-33.000952.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-management|5_2024-01-13T22-02-33.000952.parquet"]}]}, {"config_name": "harness_hendrycksTest_marketing_5", "data_files": [{"split": "2024_01_13T22_02_33.000952", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-13T22-02-33.000952.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-13T22-02-33.000952.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2024_01_13T22_02_33.000952", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T22-02-33.000952.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T22-02-33.000952.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2024_01_13T22_02_33.000952", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T22-02-33.000952.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T22-02-33.000952.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2024_01_13T22_02_33.000952", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T22-02-33.000952.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T22-02-33.000952.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2024_01_13T22_02_33.000952", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T22-02-33.000952.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T22-02-33.000952.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2024_01_13T22_02_33.000952", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T22-02-33.000952.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T22-02-33.000952.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2024_01_13T22_02_33.000952", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T22-02-33.000952.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T22-02-33.000952.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2024_01_13T22_02_33.000952", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T22-02-33.000952.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T22-02-33.000952.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2024_01_13T22_02_33.000952", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T22-02-33.000952.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T22-02-33.000952.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2024_01_13T22_02_33.000952", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T22-02-33.000952.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T22-02-33.000952.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_01_13T22_02_33.000952", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T22-02-33.000952.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T22-02-33.000952.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_01_13T22_02_33.000952", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T22-02-33.000952.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T22-02-33.000952.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_01_13T22_02_33.000952", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T22-02-33.000952.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T22-02-33.000952.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_01_13T22_02_33.000952", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T22-02-33.000952.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T22-02-33.000952.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_01_13T22_02_33.000952", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T22-02-33.000952.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T22-02-33.000952.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_01_13T22_02_33.000952", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T22-02-33.000952.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T22-02-33.000952.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_13T22_02_33.000952", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T22-02-33.000952.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T22-02-33.000952.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_13T22_02_33.000952", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T22-02-33.000952.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T22-02-33.000952.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_13T22_02_33.000952", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T22-02-33.000952.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T22-02-33.000952.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T22_02_33.000952", "path": ["**/details_harness|winogrande|5_2024-01-13T22-02-33.000952.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T22-02-33.000952.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T22_02_33.000952", "path": ["results_2024-01-13T22-02-33.000952.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T22-02-33.000952.parquet"]}]}]} | 2024-01-13T22:04:12+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of kevin009/flyingllama
Dataset automatically created during the evaluation run of model kevin009/flyingllama on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-13T22:02:33.000952(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of kevin009/flyingllama\n\n\n\nDataset automatically created during the evaluation run of model kevin009/flyingllama on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T22:02:33.000952(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of kevin009/flyingllama\n\n\n\nDataset automatically created during the evaluation run of model kevin009/flyingllama on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T22:02:33.000952(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
f83d258c98a04a03ac7a5d5e971f476246e48fc8 |
An augmented and further modified version of the AICG RP logs present in the [Nothing](https://huggingface.co/datasets/noznarb/nothing) archive dataset in Fastchat format, modified in the following ways:
- The first prompt is modified to add context and simple references to aspects of the conversation (OOC, use of emojis, content).
- All conversations were re-constructed into a single seamless conversation, without splits, as much as possible. This is ideal for training long-context models and the main reason you'd want to use this version of the dataset.
- Repeated conversations that go down different paths were merged, keeping the longest unique path through the conversation tree.
- Repeated requests by the user to ignore alignment are removed. You no longer need this if you are fine-tuning an uncensored base model (and they reduce the quality of the training). | grimulkan/aicg-logs-augmented | [
"license:unknown",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T22:13:05+00:00 | {"license": "unknown", "tags": ["not-for-all-audiences"]} | 2024-01-24T00:01:01+00:00 | [] | [] | TAGS
#license-unknown #not-for-all-audiences #region-us
|
An augmented and further modified version of the AICG RP logs present in the Nothing archive dataset in Fastchat format, modified in the following ways:
- The first prompt is modified to add context and simple references to aspects of the conversation (OOC, use of emojis, content).
- All conversations were re-constructed into a single seamless conversation, without splits, as much as possible. This is ideal for training long-context models and the main reason you'd want to use this version of the dataset.
- Repeated conversations that go down different paths were merged, keeping the longest unique path through the conversation tree.
- Repeated requests by the user to ignore alignment are removed. You no longer need this if you are fine-tuning an uncensored base model (and they reduce the quality of the training). | [] | [
"TAGS\n#license-unknown #not-for-all-audiences #region-us \n"
] |
997ad7ab66153279f758427131b4bfa7f76bcb07 |
# Dataset of murasaki/紫/紫 (Azur Lane)
This is the dataset of murasaki/紫/紫 (Azur Lane), containing 30 images and their tags.
The core tags of this character are `long_hair, purple_hair, breasts, purple_eyes, hair_ribbon, ribbon, large_breasts, very_long_hair, black_ribbon`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 30 | 66.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/murasaki_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 30 | 28.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/murasaki_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 77 | 63.68 MiB | [Download](https://huggingface.co/datasets/CyberHarem/murasaki_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 30 | 52.43 MiB | [Download](https://huggingface.co/datasets/CyberHarem/murasaki_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 77 | 98.70 MiB | [Download](https://huggingface.co/datasets/CyberHarem/murasaki_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/murasaki_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------|
| 0 | 12 |  |  |  |  |  | 1girl, dress, solo, looking_at_viewer, blush, jewelry, stuffed_animal |
| 1 | 6 |  |  |  |  |  | 1girl, solo, blush, cleavage, looking_at_viewer, navel, bangs, bare_shoulders, bra, collarbone, huge_breasts |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | dress | solo | looking_at_viewer | blush | jewelry | stuffed_animal | cleavage | navel | bangs | bare_shoulders | bra | collarbone | huge_breasts |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-------|:--------------------|:--------|:----------|:-----------------|:-----------|:--------|:--------|:-----------------|:------|:-------------|:---------------|
| 0 | 12 |  |  |  |  |  | X | X | X | X | X | X | X | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | | X | X | X | | | X | X | X | X | X | X | X |
| CyberHarem/murasaki_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T22:18:15+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T22:28:51+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of murasaki/紫/紫 (Azur Lane)
===================================
This is the dataset of murasaki/紫/紫 (Azur Lane), containing 30 images and their tags.
The core tags of this character are 'long\_hair, purple\_hair, breasts, purple\_eyes, hair\_ribbon, ribbon, large\_breasts, very\_long\_hair, black\_ribbon', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
5ceab1ca1f7505790c0e3295d536680bbb0a827f |
# Dataset of chao_ho/肇和/肇和 (Azur Lane)
This is the dataset of chao_ho/肇和/肇和 (Azur Lane), containing 44 images and their tags.
The core tags of this character are `multicolored_hair, two-tone_hair, white_hair, hair_bun, purple_eyes, split-color_hair, breasts, long_hair, cone_hair_bun, red_hair, double_bun, hairband, large_breasts, bangs, medium_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 44 | 70.43 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chao_ho_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 44 | 41.37 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chao_ho_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 108 | 84.03 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chao_ho_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 44 | 62.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chao_ho_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 108 | 116.23 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chao_ho_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/chao_ho_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 29 |  |  |  |  |  | bare_shoulders, detached_sleeves, chinese_clothes, cleavage_cutout, looking_at_viewer, wide_sleeves, 1girl, solo, blush, red_dress, single_thighhigh, white_thighhighs |
| 1 | 8 |  |  |  |  |  | 1girl, looking_at_viewer, bare_shoulders, feet, no_shoes, solo, blush, panties_under_pantyhose, thighband_pantyhose, black_pantyhose, fur_trim, toes, branch, purple_hair, soles, white_dress, ass, legs, sitting_in_tree, snow, very_long_hair, wide_sleeves |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | bare_shoulders | detached_sleeves | chinese_clothes | cleavage_cutout | looking_at_viewer | wide_sleeves | 1girl | solo | blush | red_dress | single_thighhigh | white_thighhighs | feet | no_shoes | panties_under_pantyhose | thighband_pantyhose | black_pantyhose | fur_trim | toes | branch | purple_hair | soles | white_dress | ass | legs | sitting_in_tree | snow | very_long_hair |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------|:-------------------|:------------------|:------------------|:--------------------|:---------------|:--------|:-------|:--------|:------------|:-------------------|:-------------------|:-------|:-----------|:--------------------------|:----------------------|:------------------|:-----------|:-------|:---------|:--------------|:--------|:--------------|:------|:-------|:------------------|:-------|:-----------------|
| 0 | 29 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | |
| 1 | 8 |  |  |  |  |  | X | | | | X | X | X | X | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/chao_ho_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T22:18:18+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T22:33:04+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of chao\_ho/肇和/肇和 (Azur Lane)
=====================================
This is the dataset of chao\_ho/肇和/肇和 (Azur Lane), containing 44 images and their tags.
The core tags of this character are 'multicolored\_hair, two-tone\_hair, white\_hair, hair\_bun, purple\_eyes, split-color\_hair, breasts, long\_hair, cone\_hair\_bun, red\_hair, double\_bun, hairband, large\_breasts, bangs, medium\_breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
a671f1d9a82cc3a28c7301c328a8269baf8cd084 |
# Dataset of west_virginia/ウェストバージニア/西弗吉尼亚 (Azur Lane)
This is the dataset of west_virginia/ウェストバージニア/西弗吉尼亚 (Azur Lane), containing 23 images and their tags.
The core tags of this character are `breasts, long_hair, bangs, black_hair, red_eyes, mole, large_breasts, mole_under_eye, blue_hair, earrings, medium_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 23 | 30.72 MiB | [Download](https://huggingface.co/datasets/CyberHarem/west_virginia_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 23 | 17.23 MiB | [Download](https://huggingface.co/datasets/CyberHarem/west_virginia_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 50 | 31.90 MiB | [Download](https://huggingface.co/datasets/CyberHarem/west_virginia_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 23 | 26.41 MiB | [Download](https://huggingface.co/datasets/CyberHarem/west_virginia_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 50 | 47.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/west_virginia_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/west_virginia_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 6 |  |  |  |  |  | bare_shoulders, looking_at_viewer, thighs, 1girl, mouth_mask, off_shoulder, solo, thigh_strap, very_long_hair, bare_legs, black_footwear, colored_inner_hair, covered_mouth, dress, full_body, long_legs, long_sleeves, cleavage_cutout, jacket, open_coat, panties |
| 1 | 14 |  |  |  |  |  | bare_shoulders, looking_at_viewer, 1girl, solo, dress, off_shoulder, black_gloves, coat, sleeveless, jewelry, simple_background, white_background, anchor_symbol, long_sleeves |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | bare_shoulders | looking_at_viewer | thighs | 1girl | mouth_mask | off_shoulder | solo | thigh_strap | very_long_hair | bare_legs | black_footwear | colored_inner_hair | covered_mouth | dress | full_body | long_legs | long_sleeves | cleavage_cutout | jacket | open_coat | panties | black_gloves | coat | sleeveless | jewelry | simple_background | white_background | anchor_symbol |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------|:--------------------|:---------|:--------|:-------------|:---------------|:-------|:--------------|:-----------------|:------------|:-----------------|:---------------------|:----------------|:--------|:------------|:------------|:---------------|:------------------|:---------|:------------|:----------|:---------------|:-------|:-------------|:----------|:--------------------|:-------------------|:----------------|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | |
| 1 | 14 |  |  |  |  |  | X | X | | X | | X | X | | | | | | | X | | | X | | | | | X | X | X | X | X | X | X |
| CyberHarem/west_virginia_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T22:18:22+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T22:25:07+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of west\_virginia/ウェストバージニア/西弗吉尼亚 (Azur Lane)
=====================================================
This is the dataset of west\_virginia/ウェストバージニア/西弗吉尼亚 (Azur Lane), containing 23 images and their tags.
The core tags of this character are 'breasts, long\_hair, bangs, black\_hair, red\_eyes, mole, large\_breasts, mole\_under\_eye, blue\_hair, earrings, medium\_breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
0f276a47d174c0d89d409a33e982503dc7430a73 |
# Dataset of mogami/最上/最上 (Azur Lane)
This is the dataset of mogami/最上/最上 (Azur Lane), containing 19 images and their tags.
The core tags of this character are `brown_hair, horns, single_horn, pointy_ears, breasts, red_eyes, long_hair, medium_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 19 | 18.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mogami_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 19 | 13.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mogami_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 39 | 23.32 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mogami_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 19 | 17.73 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mogami_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 39 | 28.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mogami_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/mogami_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------|
| 0 | 19 |  |  |  |  |  | 1girl, solo, looking_at_viewer, blush, detached_sleeves, thighhighs, white_background, wide_sleeves |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | blush | detached_sleeves | thighhighs | white_background | wide_sleeves |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:--------|:-------------------|:-------------|:-------------------|:---------------|
| 0 | 19 |  |  |  |  |  | X | X | X | X | X | X | X | X |
| CyberHarem/mogami_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T22:18:25+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T22:24:32+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of mogami/最上/最上 (Azur Lane)
===================================
This is the dataset of mogami/最上/最上 (Azur Lane), containing 19 images and their tags.
The core tags of this character are 'brown\_hair, horns, single\_horn, pointy\_ears, breasts, red\_eyes, long\_hair, medium\_breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
59935a9c094b9d3d27a3233584b1905a79e462f5 |
# Dataset of houston/ヒューストン/休斯敦 (Azur Lane)
This is the dataset of houston/ヒューストン/休斯敦 (Azur Lane), containing 16 images and their tags.
The core tags of this character are `green_eyes, pink_hair, long_hair, two_side_up, breasts, ahoge, bangs, small_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 16 | 15.99 MiB | [Download](https://huggingface.co/datasets/CyberHarem/houston_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 16 | 10.29 MiB | [Download](https://huggingface.co/datasets/CyberHarem/houston_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 39 | 22.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/houston_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 16 | 14.41 MiB | [Download](https://huggingface.co/datasets/CyberHarem/houston_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 39 | 28.78 MiB | [Download](https://huggingface.co/datasets/CyberHarem/houston_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/houston_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 16 |  |  |  |  |  | 1girl, looking_at_viewer, blush, bare_shoulders, navel, smile, solo, star_(symbol), open_mouth, collarbone, shorts, black_choker, midriff, criss-cross_halter, red_gloves, simple_background, stomach, thighhighs |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | blush | bare_shoulders | navel | smile | solo | star_(symbol) | open_mouth | collarbone | shorts | black_choker | midriff | criss-cross_halter | red_gloves | simple_background | stomach | thighhighs |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:--------|:-----------------|:--------|:--------|:-------|:----------------|:-------------|:-------------|:---------|:---------------|:----------|:---------------------|:-------------|:--------------------|:----------|:-------------|
| 0 | 16 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/houston_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T22:18:37+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T22:23:41+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of houston/ヒューストン/休斯敦 (Azur Lane)
=========================================
This is the dataset of houston/ヒューストン/休斯敦 (Azur Lane), containing 16 images and their tags.
The core tags of this character are 'green\_eyes, pink\_hair, long\_hair, two\_side\_up, breasts, ahoge, bangs, small\_breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
663d34a42910e3e84de4692a38bfb711ec451b26 |
# Dataset Card for Evaluation run of SanjiWatsuki/Kunoichi-DPO-v2-7B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_SanjiWatsuki__Kunoichi-DPO-v2-7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-18T22:09:51.454026](https://huggingface.co/datasets/open-llm-leaderboard/details_SanjiWatsuki__Kunoichi-DPO-v2-7B/blob/main/results_2024-01-18T22-09-51.454026.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6533767863663313,
"acc_stderr": 0.0320841379180863,
"acc_norm": 0.6540292659740939,
"acc_norm_stderr": 0.03273629792079274,
"mc1": 0.5018359853121175,
"mc1_stderr": 0.017503383046877048,
"mc2": 0.6605635432197811,
"mc2_stderr": 0.015348982161720861
},
"harness|arc:challenge|25": {
"acc": 0.6689419795221843,
"acc_stderr": 0.013752062419817836,
"acc_norm": 0.6962457337883959,
"acc_norm_stderr": 0.01343890918477877
},
"harness|hellaswag|10": {
"acc": 0.7029476199960167,
"acc_stderr": 0.00456025908319737,
"acc_norm": 0.8744274048994224,
"acc_norm_stderr": 0.0033068982422344924
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6296296296296297,
"acc_stderr": 0.041716541613545426,
"acc_norm": 0.6296296296296297,
"acc_norm_stderr": 0.041716541613545426
},
"harness|hendrycksTest-astronomy|5": {
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"acc_stderr": 0.0378272898086547,
"acc_norm": 0.6842105263157895,
"acc_norm_stderr": 0.0378272898086547
},
"harness|hendrycksTest-business_ethics|5": {
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"acc_stderr": 0.04852365870939099,
"acc_norm": 0.63,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7132075471698113,
"acc_stderr": 0.027834912527544067,
"acc_norm": 0.7132075471698113,
"acc_norm_stderr": 0.027834912527544067
},
"harness|hendrycksTest-college_biology|5": {
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"acc_norm": 0.7638888888888888,
"acc_norm_stderr": 0.03551446610810826
},
"harness|hendrycksTest-college_chemistry|5": {
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"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620333
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.5,
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"acc_norm": 0.5,
"acc_norm_stderr": 0.050251890762960605
},
"harness|hendrycksTest-college_mathematics|5": {
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"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6936416184971098,
"acc_stderr": 0.035149425512674394,
"acc_norm": 0.6936416184971098,
"acc_norm_stderr": 0.035149425512674394
},
"harness|hendrycksTest-college_physics|5": {
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"acc_stderr": 0.04928099597287534,
"acc_norm": 0.43137254901960786,
"acc_norm_stderr": 0.04928099597287534
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.76,
"acc_stderr": 0.04292346959909283,
"acc_norm": 0.76,
"acc_norm_stderr": 0.04292346959909283
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.6,
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"acc_norm": 0.6,
"acc_norm_stderr": 0.03202563076101735
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5087719298245614,
"acc_stderr": 0.04702880432049615,
"acc_norm": 0.5087719298245614,
"acc_norm_stderr": 0.04702880432049615
},
"harness|hendrycksTest-electrical_engineering|5": {
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"acc_norm": 0.5655172413793104,
"acc_norm_stderr": 0.04130740879555497
},
"harness|hendrycksTest-elementary_mathematics|5": {
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"acc_norm": 0.4074074074074074,
"acc_norm_stderr": 0.02530590624159063
},
"harness|hendrycksTest-formal_logic|5": {
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"acc_norm": 0.46825396825396826,
"acc_norm_stderr": 0.04463112720677171
},
"harness|hendrycksTest-global_facts|5": {
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"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7903225806451613,
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"acc_norm": 0.7903225806451613,
"acc_norm_stderr": 0.023157879349083515
},
"harness|hendrycksTest-high_school_chemistry|5": {
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"acc_norm": 0.4827586206896552,
"acc_norm_stderr": 0.035158955511656986
},
"harness|hendrycksTest-high_school_computer_science|5": {
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"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-high_school_european_history|5": {
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"acc_norm": 0.7636363636363637,
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},
"harness|hendrycksTest-high_school_geography|5": {
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},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8963730569948186,
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"acc_norm": 0.8963730569948186,
"acc_norm_stderr": 0.021995311963644237
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
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"acc_norm_stderr": 0.02375966576741229
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.337037037037037,
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"acc_norm": 0.337037037037037,
"acc_norm_stderr": 0.02882088466625326
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.7058823529411765,
"acc_stderr": 0.02959732973097809,
"acc_norm": 0.7058823529411765,
"acc_norm_stderr": 0.02959732973097809
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.36423841059602646,
"acc_stderr": 0.03929111781242742,
"acc_norm": 0.36423841059602646,
"acc_norm_stderr": 0.03929111781242742
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8477064220183487,
"acc_stderr": 0.015405084393157074,
"acc_norm": 0.8477064220183487,
"acc_norm_stderr": 0.015405084393157074
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5370370370370371,
"acc_stderr": 0.03400603625538271,
"acc_norm": 0.5370370370370371,
"acc_norm_stderr": 0.03400603625538271
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8333333333333334,
"acc_stderr": 0.026156867523931045,
"acc_norm": 0.8333333333333334,
"acc_norm_stderr": 0.026156867523931045
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8016877637130801,
"acc_stderr": 0.025955020841621112,
"acc_norm": 0.8016877637130801,
"acc_norm_stderr": 0.025955020841621112
},
"harness|hendrycksTest-human_aging|5": {
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"acc_stderr": 0.031146796482972465,
"acc_norm": 0.6860986547085202,
"acc_norm_stderr": 0.031146796482972465
},
"harness|hendrycksTest-human_sexuality|5": {
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"acc_norm_stderr": 0.03727673575596913
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.768595041322314,
"acc_stderr": 0.03849856098794088,
"acc_norm": 0.768595041322314,
"acc_norm_stderr": 0.03849856098794088
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.8148148148148148,
"acc_stderr": 0.03755265865037181,
"acc_norm": 0.8148148148148148,
"acc_norm_stderr": 0.03755265865037181
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7975460122699386,
"acc_stderr": 0.031570650789119005,
"acc_norm": 0.7975460122699386,
"acc_norm_stderr": 0.031570650789119005
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.4732142857142857,
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"acc_norm": 0.4732142857142857,
"acc_norm_stderr": 0.047389751192741546
},
"harness|hendrycksTest-management|5": {
"acc": 0.7864077669902912,
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"acc_norm": 0.7864077669902912,
"acc_norm_stderr": 0.040580420156460344
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8675213675213675,
"acc_stderr": 0.022209309073165616,
"acc_norm": 0.8675213675213675,
"acc_norm_stderr": 0.022209309073165616
},
"harness|hendrycksTest-medical_genetics|5": {
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},
"harness|hendrycksTest-miscellaneous|5": {
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},
"harness|hendrycksTest-moral_disputes|5": {
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},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.4547486033519553,
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"acc_norm_stderr": 0.016653875777524
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7222222222222222,
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"acc_norm": 0.7222222222222222,
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},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6945337620578779,
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"acc_norm": 0.6945337620578779,
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},
"harness|hendrycksTest-prehistory|5": {
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"acc_norm": 0.75,
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},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.5,
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},
"harness|hendrycksTest-professional_law|5": {
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"acc_norm": 0.46740547588005216,
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},
"harness|hendrycksTest-professional_medicine|5": {
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"acc_norm": 0.6948529411764706,
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},
"harness|hendrycksTest-professional_psychology|5": {
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"acc_norm_stderr": 0.01920660684882536
},
"harness|hendrycksTest-public_relations|5": {
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},
"harness|hendrycksTest-security_studies|5": {
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},
"harness|hendrycksTest-sociology|5": {
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"acc_norm_stderr": 0.025538433368578323
},
"harness|hendrycksTest-us_foreign_policy|5": {
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"acc_norm": 0.84,
"acc_norm_stderr": 0.03684529491774708
},
"harness|hendrycksTest-virology|5": {
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"acc_norm": 0.536144578313253,
"acc_norm_stderr": 0.038823108508905954
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8538011695906432,
"acc_stderr": 0.027097290118070806,
"acc_norm": 0.8538011695906432,
"acc_norm_stderr": 0.027097290118070806
},
"harness|truthfulqa:mc|0": {
"mc1": 0.5018359853121175,
"mc1_stderr": 0.017503383046877048,
"mc2": 0.6605635432197811,
"mc2_stderr": 0.015348982161720861
},
"harness|winogrande|5": {
"acc": 0.8082083662194159,
"acc_stderr": 0.011065209664659527
},
"harness|gsm8k|5": {
"acc": 0.6588324488248674,
"acc_stderr": 0.013059111935831497
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_SanjiWatsuki__Kunoichi-DPO-v2-7B | [
"region:us"
] | 2024-01-13T22:19:02+00:00 | {"pretty_name": "Evaluation run of SanjiWatsuki/Kunoichi-DPO-v2-7B", "dataset_summary": "Dataset automatically created during the evaluation run of model [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_SanjiWatsuki__Kunoichi-DPO-v2-7B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-18T22:09:51.454026](https://huggingface.co/datasets/open-llm-leaderboard/details_SanjiWatsuki__Kunoichi-DPO-v2-7B/blob/main/results_2024-01-18T22-09-51.454026.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6533767863663313,\n \"acc_stderr\": 0.0320841379180863,\n \"acc_norm\": 0.6540292659740939,\n \"acc_norm_stderr\": 0.03273629792079274,\n \"mc1\": 0.5018359853121175,\n \"mc1_stderr\": 0.017503383046877048,\n \"mc2\": 0.6605635432197811,\n \"mc2_stderr\": 0.015348982161720861\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6689419795221843,\n \"acc_stderr\": 0.013752062419817836,\n \"acc_norm\": 0.6962457337883959,\n \"acc_norm_stderr\": 0.01343890918477877\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7029476199960167,\n \"acc_stderr\": 0.00456025908319737,\n \"acc_norm\": 0.8744274048994224,\n \"acc_norm_stderr\": 0.0033068982422344924\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6296296296296297,\n \"acc_stderr\": 0.041716541613545426,\n \"acc_norm\": 0.6296296296296297,\n \"acc_norm_stderr\": 0.041716541613545426\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6842105263157895,\n \"acc_stderr\": 0.0378272898086547,\n \"acc_norm\": 0.6842105263157895,\n \"acc_norm_stderr\": 0.0378272898086547\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.63,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.7132075471698113,\n \"acc_stderr\": 0.027834912527544067,\n \"acc_norm\": 0.7132075471698113,\n \"acc_norm_stderr\": 0.027834912527544067\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7638888888888888,\n \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.7638888888888888,\n \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6936416184971098,\n \"acc_stderr\": 0.035149425512674394,\n \"acc_norm\": 0.6936416184971098,\n \"acc_norm_stderr\": 0.035149425512674394\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.04928099597287534,\n \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.04928099597287534\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.76,\n \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.03202563076101735,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.03202563076101735\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5087719298245614,\n \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.5087719298245614,\n \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555497,\n \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555497\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.4074074074074074,\n \"acc_stderr\": 0.02530590624159063,\n \"acc_norm\": 0.4074074074074074,\n \"acc_norm_stderr\": 0.02530590624159063\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.46825396825396826,\n \"acc_stderr\": 0.04463112720677171,\n \"acc_norm\": 0.46825396825396826,\n \"acc_norm_stderr\": 0.04463112720677171\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7903225806451613,\n \"acc_stderr\": 0.023157879349083515,\n \"acc_norm\": 0.7903225806451613,\n \"acc_norm_stderr\": 0.023157879349083515\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.4827586206896552,\n \"acc_stderr\": 0.035158955511656986,\n \"acc_norm\": 0.4827586206896552,\n \"acc_norm_stderr\": 0.035158955511656986\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.03317505930009181,\n \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.03317505930009181\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7828282828282829,\n \"acc_stderr\": 0.02937661648494563,\n \"acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.02937661648494563\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.021995311963644237,\n \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.021995311963644237\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6743589743589744,\n \"acc_stderr\": 0.02375966576741229,\n \"acc_norm\": 0.6743589743589744,\n \"acc_norm_stderr\": 0.02375966576741229\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.337037037037037,\n \"acc_stderr\": 0.02882088466625326,\n \"acc_norm\": 0.337037037037037,\n \"acc_norm_stderr\": 0.02882088466625326\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.7058823529411765,\n \"acc_stderr\": 0.02959732973097809,\n \"acc_norm\": 0.7058823529411765,\n \"acc_norm_stderr\": 0.02959732973097809\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.36423841059602646,\n \"acc_stderr\": 0.03929111781242742,\n \"acc_norm\": 0.36423841059602646,\n \"acc_norm_stderr\": 0.03929111781242742\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8477064220183487,\n \"acc_stderr\": 0.015405084393157074,\n \"acc_norm\": 0.8477064220183487,\n \"acc_norm_stderr\": 0.015405084393157074\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5370370370370371,\n \"acc_stderr\": 0.03400603625538271,\n \"acc_norm\": 0.5370370370370371,\n \"acc_norm_stderr\": 0.03400603625538271\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8333333333333334,\n \"acc_stderr\": 0.026156867523931045,\n \"acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.026156867523931045\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.8016877637130801,\n \"acc_stderr\": 0.025955020841621112,\n \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.025955020841621112\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6860986547085202,\n \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.6860986547085202,\n \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.7633587786259542,\n \"acc_stderr\": 0.03727673575596913,\n \"acc_norm\": 0.7633587786259542,\n \"acc_norm_stderr\": 0.03727673575596913\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.768595041322314,\n \"acc_stderr\": 0.03849856098794088,\n \"acc_norm\": 0.768595041322314,\n \"acc_norm_stderr\": 0.03849856098794088\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8148148148148148,\n \"acc_stderr\": 0.03755265865037181,\n \"acc_norm\": 0.8148148148148148,\n \"acc_norm_stderr\": 0.03755265865037181\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7975460122699386,\n \"acc_stderr\": 0.031570650789119005,\n \"acc_norm\": 0.7975460122699386,\n \"acc_norm_stderr\": 0.031570650789119005\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4732142857142857,\n \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.4732142857142857,\n \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8675213675213675,\n \"acc_stderr\": 0.022209309073165616,\n \"acc_norm\": 0.8675213675213675,\n \"acc_norm_stderr\": 0.022209309073165616\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8314176245210728,\n \"acc_stderr\": 0.013387895731543604,\n \"acc_norm\": 0.8314176245210728,\n \"acc_norm_stderr\": 0.013387895731543604\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7312138728323699,\n \"acc_stderr\": 0.023868003262500097,\n \"acc_norm\": 0.7312138728323699,\n \"acc_norm_stderr\": 0.023868003262500097\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4547486033519553,\n \"acc_stderr\": 0.016653875777524,\n \"acc_norm\": 0.4547486033519553,\n \"acc_norm_stderr\": 0.016653875777524\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.0256468630971379,\n \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.0256468630971379\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6945337620578779,\n \"acc_stderr\": 0.02616058445014045,\n \"acc_norm\": 0.6945337620578779,\n \"acc_norm_stderr\": 0.02616058445014045\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.02409347123262133,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.02409347123262133\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.029827499313594685,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.029827499313594685\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.46740547588005216,\n \"acc_stderr\": 0.012743072942653349,\n \"acc_norm\": 0.46740547588005216,\n \"acc_norm_stderr\": 0.012743072942653349\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.6948529411764706,\n \"acc_stderr\": 0.027971541370170595,\n \"acc_norm\": 0.6948529411764706,\n \"acc_norm_stderr\": 0.027971541370170595\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.6568627450980392,\n \"acc_stderr\": 0.01920660684882536,\n \"acc_norm\": 0.6568627450980392,\n \"acc_norm_stderr\": 0.01920660684882536\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.02826388994378459,\n \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.02826388994378459\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.845771144278607,\n \"acc_stderr\": 0.025538433368578323,\n \"acc_norm\": 0.845771144278607,\n \"acc_norm_stderr\": 0.025538433368578323\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774708,\n \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774708\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.536144578313253,\n \"acc_stderr\": 0.038823108508905954,\n \"acc_norm\": 0.536144578313253,\n \"acc_norm_stderr\": 0.038823108508905954\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.8538011695906432,\n \"acc_stderr\": 0.027097290118070806,\n \"acc_norm\": 0.8538011695906432,\n \"acc_norm_stderr\": 0.027097290118070806\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5018359853121175,\n \"mc1_stderr\": 0.017503383046877048,\n \"mc2\": 0.6605635432197811,\n \"mc2_stderr\": 0.015348982161720861\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.8082083662194159,\n \"acc_stderr\": 0.011065209664659527\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6588324488248674,\n \"acc_stderr\": 0.013059111935831497\n }\n}\n```", "repo_url": "https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2024_01_13T22_16_41.700572", "path": ["**/details_harness|arc:challenge|25_2024-01-13T22-16-41.700572.parquet"]}, {"split": "2024_01_18T22_09_51.454026", "path": ["**/details_harness|arc:challenge|25_2024-01-18T22-09-51.454026.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-01-18T22-09-51.454026.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_01_13T22_16_41.700572", "path": ["**/details_harness|gsm8k|5_2024-01-13T22-16-41.700572.parquet"]}, {"split": "2024_01_18T22_09_51.454026", "path": ["**/details_harness|gsm8k|5_2024-01-18T22-09-51.454026.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-01-18T22-09-51.454026.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_01_13T22_16_41.700572", "path": ["**/details_harness|hellaswag|10_2024-01-13T22-16-41.700572.parquet"]}, {"split": "2024_01_18T22_09_51.454026", "path": ["**/details_harness|hellaswag|10_2024-01-18T22-09-51.454026.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-01-18T22-09-51.454026.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_01_13T22_16_41.700572", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T22-16-41.700572.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-01-13T22-16-41.700572.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-01-13T22-16-41.700572.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T22-16-41.700572.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T22-16-41.700572.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-01-13T22-16-41.700572.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T22-16-41.700572.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T22-16-41.700572.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T22-16-41.700572.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T22-16-41.700572.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-01-13T22-16-41.700572.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-01-13T22-16-41.700572.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T22-16-41.700572.parquet", "**/details_harness|hendrycksTest-econometrics|5_2024-01-13T22-16-41.700572.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T22-16-41.700572.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T22-16-41.700572.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T22-16-41.700572.parquet", "**/details_harness|hendrycksTest-global_facts|5_2024-01-13T22-16-41.700572.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T22-16-41.700572.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T22-16-41.700572.parquet", 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["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-18T22-09-51.454026.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-18T22-09-51.454026.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2024_01_13T22_16_41.700572", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T22-16-41.700572.parquet"]}, {"split": "2024_01_18T22_09_51.454026", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-18T22-09-51.454026.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-18T22-09-51.454026.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2024_01_13T22_16_41.700572", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T22-16-41.700572.parquet"]}, {"split": "2024_01_18T22_09_51.454026", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-18T22-09-51.454026.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-18T22-09-51.454026.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2024_01_13T22_16_41.700572", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T22-16-41.700572.parquet"]}, {"split": "2024_01_18T22_09_51.454026", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-18T22-09-51.454026.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-18T22-09-51.454026.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2024_01_13T22_16_41.700572", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T22-16-41.700572.parquet"]}, {"split": "2024_01_18T22_09_51.454026", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-18T22-09-51.454026.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-18T22-09-51.454026.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2024_01_13T22_16_41.700572", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T22-16-41.700572.parquet"]}, {"split": "2024_01_18T22_09_51.454026", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-18T22-09-51.454026.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-18T22-09-51.454026.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2024_01_13T22_16_41.700572", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T22-16-41.700572.parquet"]}, {"split": "2024_01_18T22_09_51.454026", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-18T22-09-51.454026.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-18T22-09-51.454026.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_01_13T22_16_41.700572", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T22-16-41.700572.parquet"]}, {"split": "2024_01_18T22_09_51.454026", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-18T22-09-51.454026.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-18T22-09-51.454026.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_01_13T22_16_41.700572", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T22-16-41.700572.parquet"]}, {"split": "2024_01_18T22_09_51.454026", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-18T22-09-51.454026.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-18T22-09-51.454026.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_01_13T22_16_41.700572", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T22-16-41.700572.parquet"]}, {"split": "2024_01_18T22_09_51.454026", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-18T22-09-51.454026.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-18T22-09-51.454026.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_01_13T22_16_41.700572", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T22-16-41.700572.parquet"]}, {"split": "2024_01_18T22_09_51.454026", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-18T22-09-51.454026.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-18T22-09-51.454026.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_01_13T22_16_41.700572", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T22-16-41.700572.parquet"]}, {"split": "2024_01_18T22_09_51.454026", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-18T22-09-51.454026.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-18T22-09-51.454026.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_01_13T22_16_41.700572", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T22-16-41.700572.parquet"]}, {"split": "2024_01_18T22_09_51.454026", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-18T22-09-51.454026.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-18T22-09-51.454026.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_13T22_16_41.700572", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T22-16-41.700572.parquet"]}, {"split": "2024_01_18T22_09_51.454026", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-18T22-09-51.454026.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-18T22-09-51.454026.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_13T22_16_41.700572", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T22-16-41.700572.parquet"]}, {"split": "2024_01_18T22_09_51.454026", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-18T22-09-51.454026.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-18T22-09-51.454026.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_13T22_16_41.700572", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T22-16-41.700572.parquet"]}, {"split": "2024_01_18T22_09_51.454026", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-18T22-09-51.454026.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-18T22-09-51.454026.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T22_16_41.700572", "path": ["**/details_harness|winogrande|5_2024-01-13T22-16-41.700572.parquet"]}, {"split": "2024_01_18T22_09_51.454026", "path": ["**/details_harness|winogrande|5_2024-01-18T22-09-51.454026.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-18T22-09-51.454026.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T22_16_41.700572", "path": ["results_2024-01-13T22-16-41.700572.parquet"]}, {"split": "2024_01_18T22_09_51.454026", "path": ["results_2024-01-18T22-09-51.454026.parquet"]}, {"split": "latest", "path": ["results_2024-01-18T22-09-51.454026.parquet"]}]}]} | 2024-01-18T22:12:33+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of SanjiWatsuki/Kunoichi-DPO-v2-7B
Dataset automatically created during the evaluation run of model SanjiWatsuki/Kunoichi-DPO-v2-7B on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-18T22:09:51.454026(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of SanjiWatsuki/Kunoichi-DPO-v2-7B\n\n\n\nDataset automatically created during the evaluation run of model SanjiWatsuki/Kunoichi-DPO-v2-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-18T22:09:51.454026(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of SanjiWatsuki/Kunoichi-DPO-v2-7B\n\n\n\nDataset automatically created during the evaluation run of model SanjiWatsuki/Kunoichi-DPO-v2-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-18T22:09:51.454026(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
e60fbbde63d74523599d9f2ac0684a33b68418f9 |
# Dataset of gsh_18/GSh-18/GSh-18 (Girls' Frontline)
This is the dataset of gsh_18/GSh-18/GSh-18 (Girls' Frontline), containing 31 images and their tags.
The core tags of this character are `black_hair, hair_ornament, red_eyes, ahoge, bangs, hairclip, ponytail, bow, breasts, hat, nurse_cap`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 31 | 16.76 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gsh_18_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 31 | 13.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gsh_18_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 66 | 25.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gsh_18_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 31 | 16.41 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gsh_18_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 66 | 29.16 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gsh_18_girlsfrontline/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/gsh_18_girlsfrontline',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 9 |  |  |  |  |  | white_gloves, nurse, 1girl, blush, solo, alternate_costume, apron, side_ponytail, armband, looking_at_viewer, open_mouth |
| 1 | 6 |  |  |  |  |  | 1girl, looking_at_viewer, solo, white_gloves, blush, hair_ribbon, open_mouth, short_sleeves, white_background, white_pantyhose, bag, black_skirt, pleated_skirt |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | white_gloves | nurse | 1girl | blush | solo | alternate_costume | apron | side_ponytail | armband | looking_at_viewer | open_mouth | hair_ribbon | short_sleeves | white_background | white_pantyhose | bag | black_skirt | pleated_skirt |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------|:--------|:--------|:--------|:-------|:--------------------|:--------|:----------------|:----------|:--------------------|:-------------|:--------------|:----------------|:-------------------|:------------------|:------|:--------------|:----------------|
| 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | | X | X | X | | | | | X | X | X | X | X | X | X | X | X |
| CyberHarem/gsh_18_girlsfrontline | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T22:21:38+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T22:25:59+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of gsh\_18/GSh-18/GSh-18 (Girls' Frontline)
===================================================
This is the dataset of gsh\_18/GSh-18/GSh-18 (Girls' Frontline), containing 31 images and their tags.
The core tags of this character are 'black\_hair, hair\_ornament, red\_eyes, ahoge, bangs, hairclip, ponytail, bow, breasts, hat, nurse\_cap', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
1f4b2e7a8a74a2d77dd57a2ca43ab47b5bd370ee |
# Dataset of usas_12/USAS-12/USAS-12 (Girls' Frontline)
This is the dataset of usas_12/USAS-12/USAS-12 (Girls' Frontline), containing 23 images and their tags.
The core tags of this character are `long_hair, purple_eyes, hat, bangs, very_long_hair, beret, grey_hair, ribbon, hair_between_eyes, black_headwear`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 23 | 33.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/usas_12_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 23 | 20.38 MiB | [Download](https://huggingface.co/datasets/CyberHarem/usas_12_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 50 | 39.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/usas_12_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 23 | 29.91 MiB | [Download](https://huggingface.co/datasets/CyberHarem/usas_12_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 50 | 54.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/usas_12_girlsfrontline/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/usas_12_girlsfrontline',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 23 |  |  |  |  |  | 1girl, looking_at_viewer, fingerless_gloves, necktie, solo, black_gloves, blush, black_jacket, pleated_skirt, black_skirt, gun, short_sleeves, smile, white_thighhighs, closed_mouth, collared_shirt, holding, open_jacket, white_shirt |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | fingerless_gloves | necktie | solo | black_gloves | blush | black_jacket | pleated_skirt | black_skirt | gun | short_sleeves | smile | white_thighhighs | closed_mouth | collared_shirt | holding | open_jacket | white_shirt |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:--------------------|:----------|:-------|:---------------|:--------|:---------------|:----------------|:--------------|:------|:----------------|:--------|:-------------------|:---------------|:-----------------|:----------|:--------------|:--------------|
| 0 | 23 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/usas_12_girlsfrontline | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T22:21:39+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T22:26:51+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of usas\_12/USAS-12/USAS-12 (Girls' Frontline)
======================================================
This is the dataset of usas\_12/USAS-12/USAS-12 (Girls' Frontline), containing 23 images and their tags.
The core tags of this character are 'long\_hair, purple\_eyes, hat, bangs, very\_long\_hair, beret, grey\_hair, ribbon, hair\_between\_eyes, black\_headwear', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
db6fda4311bd7f3b36acf9e8c4b2255d98c133fc |
# Dataset Card for Evaluation run of chanwit/flux-7b-v0.1
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [chanwit/flux-7b-v0.1](https://huggingface.co/chanwit/flux-7b-v0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_chanwit__flux-7b-v0.1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T22:25:20.507875](https://huggingface.co/datasets/open-llm-leaderboard/details_chanwit__flux-7b-v0.1/blob/main/results_2024-01-13T22-25-20.507875.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6576135500935923,
"acc_stderr": 0.03184575998004267,
"acc_norm": 0.6577957033968994,
"acc_norm_stderr": 0.03249734268240439,
"mc1": 0.3733170134638923,
"mc1_stderr": 0.01693237055757063,
"mc2": 0.5505210495184077,
"mc2_stderr": 0.015617590489404845
},
"harness|arc:challenge|25": {
"acc": 0.6407849829351536,
"acc_stderr": 0.014020224155839159,
"acc_norm": 0.6706484641638225,
"acc_norm_stderr": 0.013734057652635474
},
"harness|hellaswag|10": {
"acc": 0.6820354511053575,
"acc_stderr": 0.004647338877642188,
"acc_norm": 0.8617805218084047,
"acc_norm_stderr": 0.0034442484997916556
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.29,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.29,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6,
"acc_stderr": 0.04232073695151589,
"acc_norm": 0.6,
"acc_norm_stderr": 0.04232073695151589
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7171052631578947,
"acc_stderr": 0.03665349695640767,
"acc_norm": 0.7171052631578947,
"acc_norm_stderr": 0.03665349695640767
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.63,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.63,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7018867924528301,
"acc_stderr": 0.02815283794249387,
"acc_norm": 0.7018867924528301,
"acc_norm_stderr": 0.02815283794249387
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7708333333333334,
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"acc_norm": 0.7708333333333334,
"acc_norm_stderr": 0.03514697467862388
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.5,
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"acc_norm": 0.5,
"acc_norm_stderr": 0.050251890762960605
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.56,
"acc_stderr": 0.049888765156985884,
"acc_norm": 0.56,
"acc_norm_stderr": 0.049888765156985884
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252604,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252604
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6705202312138728,
"acc_stderr": 0.03583901754736412,
"acc_norm": 0.6705202312138728,
"acc_norm_stderr": 0.03583901754736412
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.39215686274509803,
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"acc_norm": 0.39215686274509803,
"acc_norm_stderr": 0.04858083574266345
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.76,
"acc_stderr": 0.042923469599092816,
"acc_norm": 0.76,
"acc_norm_stderr": 0.042923469599092816
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5872340425531914,
"acc_stderr": 0.03218471141400351,
"acc_norm": 0.5872340425531914,
"acc_norm_stderr": 0.03218471141400351
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4824561403508772,
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"acc_norm": 0.4824561403508772,
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},
"harness|hendrycksTest-electrical_engineering|5": {
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"acc_norm_stderr": 0.04144311810878152
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.42857142857142855,
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"acc_norm": 0.42857142857142855,
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},
"harness|hendrycksTest-formal_logic|5": {
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"acc_norm": 0.4523809523809524,
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},
"harness|hendrycksTest-global_facts|5": {
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"acc_norm": 0.41,
"acc_norm_stderr": 0.04943110704237102
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7935483870967742,
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"acc_norm": 0.7935483870967742,
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},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5073891625615764,
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"acc_norm": 0.5073891625615764,
"acc_norm_stderr": 0.035176035403610105
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.7,
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"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7696969696969697,
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"acc_norm": 0.7696969696969697,
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},
"harness|hendrycksTest-high_school_geography|5": {
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},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9067357512953368,
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"acc_norm": 0.9067357512953368,
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},
"harness|hendrycksTest-high_school_macroeconomics|5": {
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"acc_norm": 0.6692307692307692,
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},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.37037037037037035,
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"acc_norm": 0.37037037037037035,
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},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6974789915966386,
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"acc_norm": 0.6974789915966386,
"acc_norm_stderr": 0.029837962388291932
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.33774834437086093,
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"acc_norm": 0.33774834437086093,
"acc_norm_stderr": 0.0386155754625517
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8440366972477065,
"acc_stderr": 0.015555802713590167,
"acc_norm": 0.8440366972477065,
"acc_norm_stderr": 0.015555802713590167
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5138888888888888,
"acc_stderr": 0.034086558679777494,
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"acc_norm_stderr": 0.034086558679777494
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8431372549019608,
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"acc_norm": 0.8431372549019608,
"acc_norm_stderr": 0.02552472232455335
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.810126582278481,
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},
"harness|hendrycksTest-human_aging|5": {
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},
"harness|hendrycksTest-human_sexuality|5": {
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"acc_norm_stderr": 0.03498149385462472
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8181818181818182,
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"acc_norm": 0.8181818181818182,
"acc_norm_stderr": 0.03520893951097653
},
"harness|hendrycksTest-jurisprudence|5": {
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"acc_norm": 0.7962962962962963,
"acc_norm_stderr": 0.03893542518824847
},
"harness|hendrycksTest-logical_fallacies|5": {
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},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.5089285714285714,
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"acc_norm_stderr": 0.04745033255489123
},
"harness|hendrycksTest-management|5": {
"acc": 0.8446601941747572,
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"acc_norm": 0.8446601941747572,
"acc_norm_stderr": 0.035865947385739755
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8547008547008547,
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},
"harness|hendrycksTest-medical_genetics|5": {
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},
"harness|hendrycksTest-miscellaneous|5": {
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},
"harness|hendrycksTest-moral_disputes|5": {
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},
"harness|hendrycksTest-moral_scenarios|5": {
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},
"harness|hendrycksTest-nutrition|5": {
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},
"harness|hendrycksTest-philosophy|5": {
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},
"harness|hendrycksTest-prehistory|5": {
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},
"harness|hendrycksTest-professional_accounting|5": {
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},
"harness|hendrycksTest-professional_law|5": {
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},
"harness|hendrycksTest-professional_medicine|5": {
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},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6862745098039216,
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},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6818181818181818,
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"acc_norm_stderr": 0.04461272175910509
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7346938775510204,
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"acc_norm": 0.7346938775510204,
"acc_norm_stderr": 0.028263889943784593
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8656716417910447,
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"acc_norm": 0.8656716417910447,
"acc_norm_stderr": 0.024112678240900808
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.85,
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"acc_norm": 0.85,
"acc_norm_stderr": 0.03588702812826371
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5421686746987951,
"acc_stderr": 0.0387862677100236,
"acc_norm": 0.5421686746987951,
"acc_norm_stderr": 0.0387862677100236
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.847953216374269,
"acc_stderr": 0.027539122889061463,
"acc_norm": 0.847953216374269,
"acc_norm_stderr": 0.027539122889061463
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3733170134638923,
"mc1_stderr": 0.01693237055757063,
"mc2": 0.5505210495184077,
"mc2_stderr": 0.015617590489404845
},
"harness|winogrande|5": {
"acc": 0.7900552486187845,
"acc_stderr": 0.01144628062926263
},
"harness|gsm8k|5": {
"acc": 0.7240333586050038,
"acc_stderr": 0.012312603010427352
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
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#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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## Bias, Risks, and Limitations
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_chanwit__flux-7b-v0.1 | [
"region:us"
] | 2024-01-13T22:27:49+00:00 | {"pretty_name": "Evaluation run of chanwit/flux-7b-v0.1", "dataset_summary": "Dataset automatically created during the evaluation run of model [chanwit/flux-7b-v0.1](https://huggingface.co/chanwit/flux-7b-v0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_chanwit__flux-7b-v0.1\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T22:25:20.507875](https://huggingface.co/datasets/open-llm-leaderboard/details_chanwit__flux-7b-v0.1/blob/main/results_2024-01-13T22-25-20.507875.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6576135500935923,\n \"acc_stderr\": 0.03184575998004267,\n \"acc_norm\": 0.6577957033968994,\n \"acc_norm_stderr\": 0.03249734268240439,\n \"mc1\": 0.3733170134638923,\n \"mc1_stderr\": 0.01693237055757063,\n \"mc2\": 0.5505210495184077,\n \"mc2_stderr\": 0.015617590489404845\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6407849829351536,\n \"acc_stderr\": 0.014020224155839159,\n \"acc_norm\": 0.6706484641638225,\n \"acc_norm_stderr\": 0.013734057652635474\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6820354511053575,\n \"acc_stderr\": 0.004647338877642188,\n \"acc_norm\": 0.8617805218084047,\n \"acc_norm_stderr\": 0.0034442484997916556\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.04232073695151589,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.04232073695151589\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.7171052631578947,\n \"acc_stderr\": 0.03665349695640767,\n \"acc_norm\": 0.7171052631578947,\n \"acc_norm_stderr\": 0.03665349695640767\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.63,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.7018867924528301,\n \"acc_stderr\": 0.02815283794249387,\n \"acc_norm\": 0.7018867924528301,\n \"acc_norm_stderr\": 0.02815283794249387\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7708333333333334,\n \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n \"acc_norm_stderr\": 0.03514697467862388\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.56,\n \"acc_stderr\": 0.049888765156985884,\n \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.049888765156985884\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6705202312138728,\n \"acc_stderr\": 0.03583901754736412,\n \"acc_norm\": 0.6705202312138728,\n \"acc_norm_stderr\": 0.03583901754736412\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.39215686274509803,\n \"acc_stderr\": 0.04858083574266345,\n \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.04858083574266345\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5872340425531914,\n \"acc_stderr\": 0.03218471141400351,\n \"acc_norm\": 0.5872340425531914,\n \"acc_norm_stderr\": 0.03218471141400351\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4824561403508772,\n \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878152,\n \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878152\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.42857142857142855,\n \"acc_stderr\": 0.02548718714785938,\n \"acc_norm\": 0.42857142857142855,\n \"acc_norm_stderr\": 0.02548718714785938\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4523809523809524,\n \"acc_stderr\": 0.044518079590553275,\n \"acc_norm\": 0.4523809523809524,\n \"acc_norm_stderr\": 0.044518079590553275\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7935483870967742,\n \"acc_stderr\": 0.023025899617188712,\n \"acc_norm\": 0.7935483870967742,\n \"acc_norm_stderr\": 0.023025899617188712\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5073891625615764,\n \"acc_stderr\": 0.035176035403610105,\n \"acc_norm\": 0.5073891625615764,\n \"acc_norm_stderr\": 0.035176035403610105\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.029620227874790482,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.029620227874790482\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.020986854593289733,\n \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.020986854593289733\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6692307692307692,\n \"acc_stderr\": 0.023854795680971128,\n \"acc_norm\": 0.6692307692307692,\n \"acc_norm_stderr\": 0.023854795680971128\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.37037037037037035,\n \"acc_stderr\": 0.02944316932303154,\n \"acc_norm\": 0.37037037037037035,\n \"acc_norm_stderr\": 0.02944316932303154\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6974789915966386,\n \"acc_stderr\": 0.029837962388291932,\n \"acc_norm\": 0.6974789915966386,\n \"acc_norm_stderr\": 0.029837962388291932\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.33774834437086093,\n \"acc_stderr\": 0.0386155754625517,\n \"acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.0386155754625517\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8440366972477065,\n \"acc_stderr\": 0.015555802713590167,\n \"acc_norm\": 0.8440366972477065,\n \"acc_norm_stderr\": 0.015555802713590167\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5138888888888888,\n \"acc_stderr\": 0.034086558679777494,\n \"acc_norm\": 0.5138888888888888,\n \"acc_norm_stderr\": 0.034086558679777494\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8431372549019608,\n \"acc_stderr\": 0.02552472232455335,\n \"acc_norm\": 0.8431372549019608,\n \"acc_norm_stderr\": 0.02552472232455335\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.810126582278481,\n \"acc_stderr\": 0.02553010046023349,\n \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.02553010046023349\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.695067264573991,\n \"acc_stderr\": 0.030898610882477515,\n \"acc_norm\": 0.695067264573991,\n \"acc_norm_stderr\": 0.030898610882477515\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.03498149385462472,\n \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.03498149385462472\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.8181818181818182,\n \"acc_stderr\": 0.03520893951097653,\n \"acc_norm\": 0.8181818181818182,\n \"acc_norm_stderr\": 0.03520893951097653\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7962962962962963,\n \"acc_stderr\": 0.03893542518824847,\n \"acc_norm\": 0.7962962962962963,\n \"acc_norm_stderr\": 0.03893542518824847\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7914110429447853,\n \"acc_stderr\": 0.03192193448934725,\n \"acc_norm\": 0.7914110429447853,\n \"acc_norm_stderr\": 0.03192193448934725\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5089285714285714,\n \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.5089285714285714,\n \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.8446601941747572,\n \"acc_stderr\": 0.035865947385739755,\n \"acc_norm\": 0.8446601941747572,\n \"acc_norm_stderr\": 0.035865947385739755\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8547008547008547,\n \"acc_stderr\": 0.023086635086841407,\n \"acc_norm\": 0.8547008547008547,\n \"acc_norm_stderr\": 0.023086635086841407\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.73,\n \"acc_stderr\": 0.0446196043338474,\n \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8301404853128991,\n \"acc_stderr\": 0.013428186370608304,\n \"acc_norm\": 0.8301404853128991,\n \"acc_norm_stderr\": 0.013428186370608304\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7427745664739884,\n \"acc_stderr\": 0.023532925431044283,\n \"acc_norm\": 0.7427745664739884,\n \"acc_norm_stderr\": 0.023532925431044283\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3843575418994413,\n \"acc_stderr\": 0.016269088663959402,\n \"acc_norm\": 0.3843575418994413,\n \"acc_norm_stderr\": 0.016269088663959402\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.738562091503268,\n \"acc_stderr\": 0.025160998214292452,\n \"acc_norm\": 0.738562091503268,\n \"acc_norm_stderr\": 0.025160998214292452\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7234726688102894,\n \"acc_stderr\": 0.025403832978179604,\n \"acc_norm\": 0.7234726688102894,\n \"acc_norm_stderr\": 0.025403832978179604\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.7469135802469136,\n \"acc_stderr\": 0.024191808600712995,\n \"acc_norm\": 0.7469135802469136,\n \"acc_norm_stderr\": 0.024191808600712995\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.48226950354609927,\n \"acc_stderr\": 0.02980873964223777,\n \"acc_norm\": 0.48226950354609927,\n \"acc_norm_stderr\": 0.02980873964223777\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4830508474576271,\n \"acc_stderr\": 0.012762896889210864,\n \"acc_norm\": 0.4830508474576271,\n \"acc_norm_stderr\": 0.012762896889210864\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.7205882352941176,\n \"acc_stderr\": 0.027257202606114944,\n \"acc_norm\": 0.7205882352941176,\n \"acc_norm_stderr\": 0.027257202606114944\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.6862745098039216,\n \"acc_stderr\": 0.01877168389352818,\n \"acc_norm\": 0.6862745098039216,\n \"acc_norm_stderr\": 0.01877168389352818\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6818181818181818,\n \"acc_stderr\": 0.04461272175910509,\n \"acc_norm\": 0.6818181818181818,\n \"acc_norm_stderr\": 0.04461272175910509\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.028263889943784593,\n \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.028263889943784593\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8656716417910447,\n \"acc_stderr\": 0.024112678240900808,\n \"acc_norm\": 0.8656716417910447,\n \"acc_norm_stderr\": 0.024112678240900808\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.85,\n \"acc_stderr\": 0.03588702812826371,\n \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.03588702812826371\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5421686746987951,\n \"acc_stderr\": 0.0387862677100236,\n \"acc_norm\": 0.5421686746987951,\n \"acc_norm_stderr\": 0.0387862677100236\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.847953216374269,\n \"acc_stderr\": 0.027539122889061463,\n \"acc_norm\": 0.847953216374269,\n \"acc_norm_stderr\": 0.027539122889061463\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3733170134638923,\n \"mc1_stderr\": 0.01693237055757063,\n \"mc2\": 0.5505210495184077,\n \"mc2_stderr\": 0.015617590489404845\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7900552486187845,\n \"acc_stderr\": 0.01144628062926263\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7240333586050038,\n \"acc_stderr\": 0.012312603010427352\n }\n}\n```", "repo_url": "https://huggingface.co/chanwit/flux-7b-v0.1", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": 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#region-us
|
# Dataset Card for Evaluation run of chanwit/flux-7b-v0.1
Dataset automatically created during the evaluation run of model chanwit/flux-7b-v0.1 on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-13T22:25:20.507875(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of chanwit/flux-7b-v0.1\n\n\n\nDataset automatically created during the evaluation run of model chanwit/flux-7b-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T22:25:20.507875(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of chanwit/flux-7b-v0.1\n\n\n\nDataset automatically created during the evaluation run of model chanwit/flux-7b-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T22:25:20.507875(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
da2148b2bc45e39ba3ec7a38650907f583bce241 |
# Dataset Card for Evaluation run of Jingyu6/MergeTest-7B-slerp
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Jingyu6/MergeTest-7B-slerp](https://huggingface.co/Jingyu6/MergeTest-7B-slerp) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Jingyu6__MergeTest-7B-slerp",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T22:27:10.970794](https://huggingface.co/datasets/open-llm-leaderboard/details_Jingyu6__MergeTest-7B-slerp/blob/main/results_2024-01-13T22-27-10.970794.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6435586511149554,
"acc_stderr": 0.03211164826791609,
"acc_norm": 0.6437934877212986,
"acc_norm_stderr": 0.03276783411526557,
"mc1": 0.42962056303549573,
"mc1_stderr": 0.017329234580409098,
"mc2": 0.5979568100280714,
"mc2_stderr": 0.015157800976988994
},
"harness|arc:challenge|25": {
"acc": 0.6484641638225256,
"acc_stderr": 0.013952413699600938,
"acc_norm": 0.6774744027303754,
"acc_norm_stderr": 0.013659980894277366
},
"harness|hellaswag|10": {
"acc": 0.6698864767974507,
"acc_stderr": 0.004692926794268468,
"acc_norm": 0.8614817765385382,
"acc_norm_stderr": 0.003447370972192066
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.29,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.29,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6,
"acc_stderr": 0.04232073695151589,
"acc_norm": 0.6,
"acc_norm_stderr": 0.04232073695151589
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7039473684210527,
"acc_stderr": 0.03715062154998904,
"acc_norm": 0.7039473684210527,
"acc_norm_stderr": 0.03715062154998904
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.6,
"acc_stderr": 0.04923659639173309,
"acc_norm": 0.6,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6830188679245283,
"acc_stderr": 0.02863723563980089,
"acc_norm": 0.6830188679245283,
"acc_norm_stderr": 0.02863723563980089
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.03476590104304134,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.03476590104304134
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.47,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.47,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.49,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.49,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.29,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.29,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6473988439306358,
"acc_stderr": 0.036430371689585475,
"acc_norm": 0.6473988439306358,
"acc_norm_stderr": 0.036430371689585475
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.3431372549019608,
"acc_stderr": 0.04724007352383888,
"acc_norm": 0.3431372549019608,
"acc_norm_stderr": 0.04724007352383888
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.74,
"acc_stderr": 0.04408440022768078,
"acc_norm": 0.74,
"acc_norm_stderr": 0.04408440022768078
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5829787234042553,
"acc_stderr": 0.03223276266711712,
"acc_norm": 0.5829787234042553,
"acc_norm_stderr": 0.03223276266711712
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5087719298245614,
"acc_stderr": 0.047028804320496165,
"acc_norm": 0.5087719298245614,
"acc_norm_stderr": 0.047028804320496165
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5517241379310345,
"acc_stderr": 0.04144311810878152,
"acc_norm": 0.5517241379310345,
"acc_norm_stderr": 0.04144311810878152
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.41534391534391535,
"acc_stderr": 0.025379524910778408,
"acc_norm": 0.41534391534391535,
"acc_norm_stderr": 0.025379524910778408
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4444444444444444,
"acc_stderr": 0.044444444444444495,
"acc_norm": 0.4444444444444444,
"acc_norm_stderr": 0.044444444444444495
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.38,
"acc_stderr": 0.048783173121456316,
"acc_norm": 0.38,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7838709677419354,
"acc_stderr": 0.02341529343356853,
"acc_norm": 0.7838709677419354,
"acc_norm_stderr": 0.02341529343356853
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5024630541871922,
"acc_stderr": 0.035179450386910616,
"acc_norm": 0.5024630541871922,
"acc_norm_stderr": 0.035179450386910616
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.69,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7696969696969697,
"acc_stderr": 0.0328766675860349,
"acc_norm": 0.7696969696969697,
"acc_norm_stderr": 0.0328766675860349
},
"harness|hendrycksTest-high_school_geography|5": {
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"acc_stderr": 0.02937661648494563,
"acc_norm": 0.7828282828282829,
"acc_norm_stderr": 0.02937661648494563
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9015544041450777,
"acc_stderr": 0.02150024957603346,
"acc_norm": 0.9015544041450777,
"acc_norm_stderr": 0.02150024957603346
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6487179487179487,
"acc_stderr": 0.024203665177902803,
"acc_norm": 0.6487179487179487,
"acc_norm_stderr": 0.024203665177902803
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.32592592592592595,
"acc_stderr": 0.028578348365473082,
"acc_norm": 0.32592592592592595,
"acc_norm_stderr": 0.028578348365473082
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6890756302521008,
"acc_stderr": 0.030066761582977927,
"acc_norm": 0.6890756302521008,
"acc_norm_stderr": 0.030066761582977927
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.31788079470198677,
"acc_stderr": 0.038020397601079024,
"acc_norm": 0.31788079470198677,
"acc_norm_stderr": 0.038020397601079024
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8495412844036697,
"acc_stderr": 0.015328563932669237,
"acc_norm": 0.8495412844036697,
"acc_norm_stderr": 0.015328563932669237
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5092592592592593,
"acc_stderr": 0.034093869469927006,
"acc_norm": 0.5092592592592593,
"acc_norm_stderr": 0.034093869469927006
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8137254901960784,
"acc_stderr": 0.027325470966716312,
"acc_norm": 0.8137254901960784,
"acc_norm_stderr": 0.027325470966716312
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.810126582278481,
"acc_stderr": 0.025530100460233494,
"acc_norm": 0.810126582278481,
"acc_norm_stderr": 0.025530100460233494
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.695067264573991,
"acc_stderr": 0.030898610882477515,
"acc_norm": 0.695067264573991,
"acc_norm_stderr": 0.030898610882477515
},
"harness|hendrycksTest-human_sexuality|5": {
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"acc_stderr": 0.03547771004159465,
"acc_norm": 0.7938931297709924,
"acc_norm_stderr": 0.03547771004159465
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8099173553719008,
"acc_stderr": 0.03581796951709282,
"acc_norm": 0.8099173553719008,
"acc_norm_stderr": 0.03581796951709282
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.0401910747255735,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.0401910747255735
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7668711656441718,
"acc_stderr": 0.0332201579577674,
"acc_norm": 0.7668711656441718,
"acc_norm_stderr": 0.0332201579577674
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.44642857142857145,
"acc_stderr": 0.047184714852195886,
"acc_norm": 0.44642857142857145,
"acc_norm_stderr": 0.047184714852195886
},
"harness|hendrycksTest-management|5": {
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"acc_stderr": 0.04245022486384495,
"acc_norm": 0.7572815533980582,
"acc_norm_stderr": 0.04245022486384495
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8589743589743589,
"acc_stderr": 0.022801382534597542,
"acc_norm": 0.8589743589743589,
"acc_norm_stderr": 0.022801382534597542
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.71,
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"acc_norm": 0.71,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-miscellaneous|5": {
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"acc_norm": 0.8352490421455939,
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},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7312138728323699,
"acc_stderr": 0.023868003262500104,
"acc_norm": 0.7312138728323699,
"acc_norm_stderr": 0.023868003262500104
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.36201117318435755,
"acc_stderr": 0.016073067350153087,
"acc_norm": 0.36201117318435755,
"acc_norm_stderr": 0.016073067350153087
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7418300653594772,
"acc_stderr": 0.02505850331695814,
"acc_norm": 0.7418300653594772,
"acc_norm_stderr": 0.02505850331695814
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7202572347266881,
"acc_stderr": 0.025494259350694912,
"acc_norm": 0.7202572347266881,
"acc_norm_stderr": 0.025494259350694912
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7469135802469136,
"acc_stderr": 0.024191808600712995,
"acc_norm": 0.7469135802469136,
"acc_norm_stderr": 0.024191808600712995
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.4929078014184397,
"acc_stderr": 0.02982449855912901,
"acc_norm": 0.4929078014184397,
"acc_norm_stderr": 0.02982449855912901
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.47131681877444587,
"acc_stderr": 0.012749206007657473,
"acc_norm": 0.47131681877444587,
"acc_norm_stderr": 0.012749206007657473
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6838235294117647,
"acc_stderr": 0.028245687391462927,
"acc_norm": 0.6838235294117647,
"acc_norm_stderr": 0.028245687391462927
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6781045751633987,
"acc_stderr": 0.018901015322093092,
"acc_norm": 0.6781045751633987,
"acc_norm_stderr": 0.018901015322093092
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6545454545454545,
"acc_stderr": 0.04554619617541054,
"acc_norm": 0.6545454545454545,
"acc_norm_stderr": 0.04554619617541054
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.746938775510204,
"acc_stderr": 0.027833023871399677,
"acc_norm": 0.746938775510204,
"acc_norm_stderr": 0.027833023871399677
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.835820895522388,
"acc_stderr": 0.026193923544454115,
"acc_norm": 0.835820895522388,
"acc_norm_stderr": 0.026193923544454115
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.86,
"acc_stderr": 0.0348735088019777,
"acc_norm": 0.86,
"acc_norm_stderr": 0.0348735088019777
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5240963855421686,
"acc_stderr": 0.03887971849597264,
"acc_norm": 0.5240963855421686,
"acc_norm_stderr": 0.03887971849597264
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8304093567251462,
"acc_stderr": 0.02878210810540171,
"acc_norm": 0.8304093567251462,
"acc_norm_stderr": 0.02878210810540171
},
"harness|truthfulqa:mc|0": {
"mc1": 0.42962056303549573,
"mc1_stderr": 0.017329234580409098,
"mc2": 0.5979568100280714,
"mc2_stderr": 0.015157800976988994
},
"harness|winogrande|5": {
"acc": 0.7963693764798737,
"acc_stderr": 0.011317798781626918
},
"harness|gsm8k|5": {
"acc": 0.6974981046247157,
"acc_stderr": 0.012652544133186141
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
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### Direct Use
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[More Information Needed]
### Out-of-Scope Use
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## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## Dataset Card Authors [optional]
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_Jingyu6__MergeTest-7B-slerp | [
"region:us"
] | 2024-01-13T22:29:29+00:00 | {"pretty_name": "Evaluation run of Jingyu6/MergeTest-7B-slerp", "dataset_summary": "Dataset automatically created during the evaluation run of model [Jingyu6/MergeTest-7B-slerp](https://huggingface.co/Jingyu6/MergeTest-7B-slerp) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Jingyu6__MergeTest-7B-slerp\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T22:27:10.970794](https://huggingface.co/datasets/open-llm-leaderboard/details_Jingyu6__MergeTest-7B-slerp/blob/main/results_2024-01-13T22-27-10.970794.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6435586511149554,\n \"acc_stderr\": 0.03211164826791609,\n \"acc_norm\": 0.6437934877212986,\n \"acc_norm_stderr\": 0.03276783411526557,\n \"mc1\": 0.42962056303549573,\n \"mc1_stderr\": 0.017329234580409098,\n \"mc2\": 0.5979568100280714,\n \"mc2_stderr\": 0.015157800976988994\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6484641638225256,\n \"acc_stderr\": 0.013952413699600938,\n \"acc_norm\": 0.6774744027303754,\n \"acc_norm_stderr\": 0.013659980894277366\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6698864767974507,\n \"acc_stderr\": 0.004692926794268468,\n \"acc_norm\": 0.8614817765385382,\n \"acc_norm_stderr\": 0.003447370972192066\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.04232073695151589,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.04232073695151589\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.7039473684210527,\n \"acc_stderr\": 0.03715062154998904,\n \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.03715062154998904\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6830188679245283,\n \"acc_stderr\": 0.02863723563980089,\n \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.02863723563980089\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6473988439306358,\n \"acc_stderr\": 0.036430371689585475,\n \"acc_norm\": 0.6473988439306358,\n \"acc_norm_stderr\": 0.036430371689585475\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.3431372549019608,\n \"acc_stderr\": 0.04724007352383888,\n \"acc_norm\": 0.3431372549019608,\n \"acc_norm_stderr\": 0.04724007352383888\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768078\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5829787234042553,\n \"acc_stderr\": 0.03223276266711712,\n \"acc_norm\": 0.5829787234042553,\n \"acc_norm_stderr\": 0.03223276266711712\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5087719298245614,\n \"acc_stderr\": 0.047028804320496165,\n \"acc_norm\": 0.5087719298245614,\n \"acc_norm_stderr\": 0.047028804320496165\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878152,\n \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878152\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.41534391534391535,\n \"acc_stderr\": 0.025379524910778408,\n \"acc_norm\": 0.41534391534391535,\n \"acc_norm_stderr\": 0.025379524910778408\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4444444444444444,\n \"acc_stderr\": 0.044444444444444495,\n \"acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.044444444444444495\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7838709677419354,\n \"acc_stderr\": 0.02341529343356853,\n \"acc_norm\": 0.7838709677419354,\n \"acc_norm_stderr\": 0.02341529343356853\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5024630541871922,\n \"acc_stderr\": 0.035179450386910616,\n \"acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.035179450386910616\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7828282828282829,\n \"acc_stderr\": 0.02937661648494563,\n \"acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.02937661648494563\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.02150024957603346,\n \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.02150024957603346\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6487179487179487,\n \"acc_stderr\": 0.024203665177902803,\n \"acc_norm\": 0.6487179487179487,\n \"acc_norm_stderr\": 0.024203665177902803\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.32592592592592595,\n \"acc_stderr\": 0.028578348365473082,\n \"acc_norm\": 0.32592592592592595,\n \"acc_norm_stderr\": 0.028578348365473082\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6890756302521008,\n \"acc_stderr\": 0.030066761582977927,\n \"acc_norm\": 0.6890756302521008,\n \"acc_norm_stderr\": 0.030066761582977927\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.31788079470198677,\n \"acc_stderr\": 0.038020397601079024,\n \"acc_norm\": 0.31788079470198677,\n \"acc_norm_stderr\": 0.038020397601079024\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8495412844036697,\n \"acc_stderr\": 0.015328563932669237,\n \"acc_norm\": 0.8495412844036697,\n \"acc_norm_stderr\": 0.015328563932669237\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5092592592592593,\n \"acc_stderr\": 0.034093869469927006,\n \"acc_norm\": 0.5092592592592593,\n \"acc_norm_stderr\": 0.034093869469927006\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8137254901960784,\n \"acc_stderr\": 0.027325470966716312,\n \"acc_norm\": 0.8137254901960784,\n \"acc_norm_stderr\": 0.027325470966716312\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.810126582278481,\n \"acc_stderr\": 0.025530100460233494,\n \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.025530100460233494\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.695067264573991,\n \"acc_stderr\": 0.030898610882477515,\n \"acc_norm\": 0.695067264573991,\n \"acc_norm_stderr\": 0.030898610882477515\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.7938931297709924,\n \"acc_stderr\": 0.03547771004159465,\n \"acc_norm\": 0.7938931297709924,\n \"acc_norm_stderr\": 0.03547771004159465\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.8099173553719008,\n \"acc_stderr\": 0.03581796951709282,\n \"acc_norm\": 0.8099173553719008,\n \"acc_norm_stderr\": 0.03581796951709282\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.0401910747255735,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.0401910747255735\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7668711656441718,\n \"acc_stderr\": 0.0332201579577674,\n \"acc_norm\": 0.7668711656441718,\n \"acc_norm_stderr\": 0.0332201579577674\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.44642857142857145,\n \"acc_stderr\": 0.047184714852195886,\n \"acc_norm\": 0.44642857142857145,\n \"acc_norm_stderr\": 0.047184714852195886\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384495,\n \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384495\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8589743589743589,\n \"acc_stderr\": 0.022801382534597542,\n \"acc_norm\": 0.8589743589743589,\n \"acc_norm_stderr\": 0.022801382534597542\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8352490421455939,\n \"acc_stderr\": 0.013265346261323797,\n \"acc_norm\": 0.8352490421455939,\n \"acc_norm_stderr\": 0.013265346261323797\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7312138728323699,\n \"acc_stderr\": 0.023868003262500104,\n \"acc_norm\": 0.7312138728323699,\n \"acc_norm_stderr\": 0.023868003262500104\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.36201117318435755,\n \"acc_stderr\": 0.016073067350153087,\n \"acc_norm\": 0.36201117318435755,\n \"acc_norm_stderr\": 0.016073067350153087\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.7418300653594772,\n \"acc_stderr\": 0.02505850331695814,\n \"acc_norm\": 0.7418300653594772,\n \"acc_norm_stderr\": 0.02505850331695814\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7202572347266881,\n \"acc_stderr\": 0.025494259350694912,\n \"acc_norm\": 0.7202572347266881,\n \"acc_norm_stderr\": 0.025494259350694912\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.7469135802469136,\n \"acc_stderr\": 0.024191808600712995,\n \"acc_norm\": 0.7469135802469136,\n \"acc_norm_stderr\": 0.024191808600712995\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \"acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.47131681877444587,\n \"acc_stderr\": 0.012749206007657473,\n \"acc_norm\": 0.47131681877444587,\n \"acc_norm_stderr\": 0.012749206007657473\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.6838235294117647,\n \"acc_stderr\": 0.028245687391462927,\n \"acc_norm\": 0.6838235294117647,\n \"acc_norm_stderr\": 0.028245687391462927\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.6781045751633987,\n \"acc_stderr\": 0.018901015322093092,\n \"acc_norm\": 0.6781045751633987,\n \"acc_norm_stderr\": 0.018901015322093092\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6545454545454545,\n \"acc_stderr\": 0.04554619617541054,\n \"acc_norm\": 0.6545454545454545,\n \"acc_norm_stderr\": 0.04554619617541054\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.746938775510204,\n \"acc_stderr\": 0.027833023871399677,\n \"acc_norm\": 0.746938775510204,\n \"acc_norm_stderr\": 0.027833023871399677\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.835820895522388,\n \"acc_stderr\": 0.026193923544454115,\n \"acc_norm\": 0.835820895522388,\n \"acc_norm_stderr\": 0.026193923544454115\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.86,\n \"acc_stderr\": 0.0348735088019777,\n \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.0348735088019777\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5240963855421686,\n \"acc_stderr\": 0.03887971849597264,\n \"acc_norm\": 0.5240963855421686,\n \"acc_norm_stderr\": 0.03887971849597264\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.42962056303549573,\n \"mc1_stderr\": 0.017329234580409098,\n \"mc2\": 0.5979568100280714,\n \"mc2_stderr\": 0.015157800976988994\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7963693764798737,\n \"acc_stderr\": 0.011317798781626918\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6974981046247157,\n \"acc_stderr\": 0.012652544133186141\n }\n}\n```", "repo_url": "https://huggingface.co/Jingyu6/MergeTest-7B-slerp", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2024_01_13T22_27_10.970794", "path": ["**/details_harness|arc:challenge|25_2024-01-13T22-27-10.970794.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-01-13T22-27-10.970794.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_01_13T22_27_10.970794", "path": ["**/details_harness|gsm8k|5_2024-01-13T22-27-10.970794.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-01-13T22-27-10.970794.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_01_13T22_27_10.970794", "path": ["**/details_harness|hellaswag|10_2024-01-13T22-27-10.970794.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-01-13T22-27-10.970794.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_01_13T22_27_10.970794", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T22-27-10.970794.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-01-13T22-27-10.970794.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-01-13T22-27-10.970794.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T22-27-10.970794.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T22-27-10.970794.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-01-13T22-27-10.970794.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T22-27-10.970794.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T22-27-10.970794.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T22-27-10.970794.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T22-27-10.970794.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-01-13T22-27-10.970794.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-01-13T22-27-10.970794.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T22-27-10.970794.parquet", "**/details_harness|hendrycksTest-econometrics|5_2024-01-13T22-27-10.970794.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T22-27-10.970794.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T22-27-10.970794.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T22-27-10.970794.parquet", "**/details_harness|hendrycksTest-global_facts|5_2024-01-13T22-27-10.970794.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T22-27-10.970794.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T22-27-10.970794.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T22-27-10.970794.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-13T22-27-10.970794.parquet", 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"path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-13T22-27-10.970794.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2024_01_13T22_27_10.970794", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T22-27-10.970794.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T22-27-10.970794.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2024_01_13T22_27_10.970794", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T22-27-10.970794.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T22-27-10.970794.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2024_01_13T22_27_10.970794", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T22-27-10.970794.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T22-27-10.970794.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2024_01_13T22_27_10.970794", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T22-27-10.970794.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T22-27-10.970794.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2024_01_13T22_27_10.970794", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T22-27-10.970794.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T22-27-10.970794.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2024_01_13T22_27_10.970794", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T22-27-10.970794.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T22-27-10.970794.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2024_01_13T22_27_10.970794", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T22-27-10.970794.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T22-27-10.970794.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2024_01_13T22_27_10.970794", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T22-27-10.970794.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T22-27-10.970794.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2024_01_13T22_27_10.970794", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T22-27-10.970794.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T22-27-10.970794.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_01_13T22_27_10.970794", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T22-27-10.970794.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T22-27-10.970794.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_01_13T22_27_10.970794", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T22-27-10.970794.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T22-27-10.970794.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_01_13T22_27_10.970794", "path": 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["**/details_harness|truthfulqa:mc|0_2024-01-13T22-27-10.970794.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T22-27-10.970794.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T22_27_10.970794", "path": ["**/details_harness|winogrande|5_2024-01-13T22-27-10.970794.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T22-27-10.970794.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T22_27_10.970794", "path": ["results_2024-01-13T22-27-10.970794.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T22-27-10.970794.parquet"]}]}]} | 2024-01-13T22:29:48+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of Jingyu6/MergeTest-7B-slerp
Dataset automatically created during the evaluation run of model Jingyu6/MergeTest-7B-slerp on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-13T22:27:10.970794(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of Jingyu6/MergeTest-7B-slerp\n\n\n\nDataset automatically created during the evaluation run of model Jingyu6/MergeTest-7B-slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T22:27:10.970794(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of Jingyu6/MergeTest-7B-slerp\n\n\n\nDataset automatically created during the evaluation run of model Jingyu6/MergeTest-7B-slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T22:27:10.970794(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
9c1da005953cd90d4948effb703ff4bb965a84a3 |
# Dataset Card for Evaluation run of CallComply/openchat-3.5-0106-32k
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [CallComply/openchat-3.5-0106-32k](https://huggingface.co/CallComply/openchat-3.5-0106-32k) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_CallComply__openchat-3.5-0106-32k",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T22:31:22.930720](https://huggingface.co/datasets/open-llm-leaderboard/details_CallComply__openchat-3.5-0106-32k/blob/main/results_2024-01-13T22-31-22.930720.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6528578653707416,
"acc_stderr": 0.031849870154313474,
"acc_norm": 0.6535559561419437,
"acc_norm_stderr": 0.03250454817189663,
"mc1": 0.35862913096695226,
"mc1_stderr": 0.016789289499502022,
"mc2": 0.5189602568049447,
"mc2_stderr": 0.015303685990455876
},
"harness|arc:challenge|25": {
"acc": 0.621160409556314,
"acc_stderr": 0.014175915490000324,
"acc_norm": 0.6604095563139932,
"acc_norm_stderr": 0.01383903976282017
},
"harness|hellaswag|10": {
"acc": 0.6338378809002191,
"acc_stderr": 0.0048076995399734075,
"acc_norm": 0.8293168691495718,
"acc_norm_stderr": 0.0037546293132751625
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.35,
"acc_stderr": 0.0479372485441102,
"acc_norm": 0.35,
"acc_norm_stderr": 0.0479372485441102
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6444444444444445,
"acc_stderr": 0.04135176749720385,
"acc_norm": 0.6444444444444445,
"acc_norm_stderr": 0.04135176749720385
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6907894736842105,
"acc_stderr": 0.037610708698674805,
"acc_norm": 0.6907894736842105,
"acc_norm_stderr": 0.037610708698674805
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.65,
"acc_stderr": 0.0479372485441102,
"acc_norm": 0.65,
"acc_norm_stderr": 0.0479372485441102
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7094339622641509,
"acc_stderr": 0.02794321998933714,
"acc_norm": 0.7094339622641509,
"acc_norm_stderr": 0.02794321998933714
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7708333333333334,
"acc_stderr": 0.03514697467862388,
"acc_norm": 0.7708333333333334,
"acc_norm_stderr": 0.03514697467862388
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.57,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.57,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.38,
"acc_stderr": 0.04878317312145634,
"acc_norm": 0.38,
"acc_norm_stderr": 0.04878317312145634
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6878612716763006,
"acc_stderr": 0.03533133389323657,
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}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## Dataset Structure
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Annotation process
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#### Personal and Sensitive Information
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_CallComply__openchat-3.5-0106-32k | [
"region:us"
] | 2024-01-13T22:33:41+00:00 | {"pretty_name": "Evaluation run of CallComply/openchat-3.5-0106-32k", "dataset_summary": "Dataset automatically created during the evaluation run of model [CallComply/openchat-3.5-0106-32k](https://huggingface.co/CallComply/openchat-3.5-0106-32k) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_CallComply__openchat-3.5-0106-32k\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T22:31:22.930720](https://huggingface.co/datasets/open-llm-leaderboard/details_CallComply__openchat-3.5-0106-32k/blob/main/results_2024-01-13T22-31-22.930720.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6528578653707416,\n \"acc_stderr\": 0.031849870154313474,\n \"acc_norm\": 0.6535559561419437,\n \"acc_norm_stderr\": 0.03250454817189663,\n \"mc1\": 0.35862913096695226,\n \"mc1_stderr\": 0.016789289499502022,\n \"mc2\": 0.5189602568049447,\n \"mc2_stderr\": 0.015303685990455876\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.621160409556314,\n \"acc_stderr\": 0.014175915490000324,\n \"acc_norm\": 0.6604095563139932,\n \"acc_norm_stderr\": 0.01383903976282017\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6338378809002191,\n \"acc_stderr\": 0.0048076995399734075,\n \"acc_norm\": 0.8293168691495718,\n \"acc_norm_stderr\": 0.0037546293132751625\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6444444444444445,\n \"acc_stderr\": 0.04135176749720385,\n \"acc_norm\": 0.6444444444444445,\n \"acc_norm_stderr\": 0.04135176749720385\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6907894736842105,\n \"acc_stderr\": 0.037610708698674805,\n \"acc_norm\": 0.6907894736842105,\n \"acc_norm_stderr\": 0.037610708698674805\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.65,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.7094339622641509,\n \"acc_stderr\": 0.02794321998933714,\n \"acc_norm\": 0.7094339622641509,\n \"acc_norm_stderr\": 0.02794321998933714\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7708333333333334,\n \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n \"acc_norm_stderr\": 0.03514697467862388\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.48,\n 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"path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-13T22-31-22.930720.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2024_01_13T22_31_22.930720", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T22-31-22.930720.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T22-31-22.930720.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2024_01_13T22_31_22.930720", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T22-31-22.930720.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T22-31-22.930720.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2024_01_13T22_31_22.930720", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T22-31-22.930720.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T22-31-22.930720.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2024_01_13T22_31_22.930720", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T22-31-22.930720.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T22-31-22.930720.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2024_01_13T22_31_22.930720", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T22-31-22.930720.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T22-31-22.930720.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2024_01_13T22_31_22.930720", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T22-31-22.930720.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T22-31-22.930720.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2024_01_13T22_31_22.930720", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T22-31-22.930720.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T22-31-22.930720.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2024_01_13T22_31_22.930720", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T22-31-22.930720.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T22-31-22.930720.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2024_01_13T22_31_22.930720", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T22-31-22.930720.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T22-31-22.930720.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_01_13T22_31_22.930720", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T22-31-22.930720.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T22-31-22.930720.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_01_13T22_31_22.930720", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T22-31-22.930720.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T22-31-22.930720.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_01_13T22_31_22.930720", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T22-31-22.930720.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T22-31-22.930720.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_01_13T22_31_22.930720", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T22-31-22.930720.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T22-31-22.930720.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_01_13T22_31_22.930720", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T22-31-22.930720.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T22-31-22.930720.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_01_13T22_31_22.930720", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T22-31-22.930720.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T22-31-22.930720.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_13T22_31_22.930720", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T22-31-22.930720.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T22-31-22.930720.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_13T22_31_22.930720", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T22-31-22.930720.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T22-31-22.930720.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_13T22_31_22.930720", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T22-31-22.930720.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T22-31-22.930720.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T22_31_22.930720", "path": ["**/details_harness|winogrande|5_2024-01-13T22-31-22.930720.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T22-31-22.930720.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T22_31_22.930720", "path": ["results_2024-01-13T22-31-22.930720.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T22-31-22.930720.parquet"]}]}]} | 2024-01-13T22:34:00+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of CallComply/openchat-3.5-0106-32k
Dataset automatically created during the evaluation run of model CallComply/openchat-3.5-0106-32k on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-13T22:31:22.930720(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of CallComply/openchat-3.5-0106-32k\n\n\n\nDataset automatically created during the evaluation run of model CallComply/openchat-3.5-0106-32k on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T22:31:22.930720(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of CallComply/openchat-3.5-0106-32k\n\n\n\nDataset automatically created during the evaluation run of model CallComply/openchat-3.5-0106-32k on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T22:31:22.930720(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
bd113a6312ec16a730cf8fdfaf54e65ac92ecb26 |
# Dataset of spitfire/Spitfire/喷火 (Girls' Frontline)
This is the dataset of spitfire/Spitfire/喷火 (Girls' Frontline), containing 15 images and their tags.
The core tags of this character are `long_hair, hat, green_eyes, top_hat, grey_hair, breasts, bangs, hair_between_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 15 | 20.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/spitfire_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 15 | 11.99 MiB | [Download](https://huggingface.co/datasets/CyberHarem/spitfire_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 34 | 22.67 MiB | [Download](https://huggingface.co/datasets/CyberHarem/spitfire_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 15 | 18.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/spitfire_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 34 | 30.82 MiB | [Download](https://huggingface.co/datasets/CyberHarem/spitfire_girlsfrontline/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/spitfire_girlsfrontline',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 12 |  |  |  |  |  | 1girl, looking_at_viewer, solo, black_gloves, dress, belt, handgun, necktie, bare_shoulders, boots, brown_hair, holding_gun, official_alternate_costume, pantyhose, small_breasts, thighhighs |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | black_gloves | dress | belt | handgun | necktie | bare_shoulders | boots | brown_hair | holding_gun | official_alternate_costume | pantyhose | small_breasts | thighhighs |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:---------------|:--------|:-------|:----------|:----------|:-----------------|:--------|:-------------|:--------------|:-----------------------------|:------------|:----------------|:-------------|
| 0 | 12 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/spitfire_girlsfrontline | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T22:42:35+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T22:45:55+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of spitfire/Spitfire/喷火 (Girls' Frontline)
==================================================
This is the dataset of spitfire/Spitfire/喷火 (Girls' Frontline), containing 15 images and their tags.
The core tags of this character are 'long\_hair, hat, green\_eyes, top\_hat, grey\_hair, breasts, bangs, hair\_between\_eyes', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
1e2c49579c410741416495c106f07c136e37c738 |
# Dataset of aug_para/AUGPara/AUGSMG (Girls' Frontline)
This is the dataset of aug_para/AUGPara/AUGSMG (Girls' Frontline), containing 19 images and their tags.
The core tags of this character are `long_hair, yellow_eyes, bangs, twintails, grey_hair, bow, hair_ribbon, breasts, hair_bow, ribbon`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 19 | 28.23 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aug_para_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 19 | 17.16 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aug_para_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 46 | 34.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aug_para_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 19 | 25.99 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aug_para_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 46 | 47.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aug_para_girlsfrontline/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/aug_para_girlsfrontline',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 13 |  |  |  |  |  | 1girl, solo, blush, looking_at_viewer, dress, open_mouth, smile, holding, simple_background, white_background, bag, black_ribbon, long_sleeves, open_clothes, squirrel, thighhighs |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | blush | looking_at_viewer | dress | open_mouth | smile | holding | simple_background | white_background | bag | black_ribbon | long_sleeves | open_clothes | squirrel | thighhighs |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------|:--------------------|:--------|:-------------|:--------|:----------|:--------------------|:-------------------|:------|:---------------|:---------------|:---------------|:-----------|:-------------|
| 0 | 13 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/aug_para_girlsfrontline | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T22:42:51+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T22:47:26+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of aug\_para/AUGPara/AUGSMG (Girls' Frontline)
======================================================
This is the dataset of aug\_para/AUGPara/AUGSMG (Girls' Frontline), containing 19 images and their tags.
The core tags of this character are 'long\_hair, yellow\_eyes, bangs, twintails, grey\_hair, bow, hair\_ribbon, breasts, hair\_bow, ribbon', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
ff9bb3cdd1523a60005c828d932407d51b2f79f5 |
# Dataset of shimanto/四万十/四万十 (Azur Lane)
This is the dataset of shimanto/四万十/四万十 (Azur Lane), containing 40 images and their tags.
The core tags of this character are `breasts, long_hair, large_breasts, red_eyes, white_hair, bangs, horns, very_long_hair, dragon_girl`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 40 | 75.34 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shimanto_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 40 | 35.66 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shimanto_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 108 | 81.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shimanto_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 40 | 62.42 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shimanto_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 108 | 123.93 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shimanto_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/shimanto_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 28 |  |  |  |  |  | 1girl, solo, cleavage, looking_at_viewer, detached_sleeves, white_thighhighs, bare_shoulders, thighs, sash, blush, navel, panties, tail, white_background, parted_lips, revealing_clothes |
| 1 | 6 |  |  |  |  |  | bare_shoulders, detached_collar, looking_at_viewer, maid_headdress, white_gloves, 1girl, cleavage, detached_sleeves, solo, black_bowtie, smile, wide_sleeves, black_thighhighs, blush, frills, indoors, waist_apron, white_apron |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | cleavage | looking_at_viewer | detached_sleeves | white_thighhighs | bare_shoulders | thighs | sash | blush | navel | panties | tail | white_background | parted_lips | revealing_clothes | detached_collar | maid_headdress | white_gloves | black_bowtie | smile | wide_sleeves | black_thighhighs | frills | indoors | waist_apron | white_apron |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:-----------|:--------------------|:-------------------|:-------------------|:-----------------|:---------|:-------|:--------|:--------|:----------|:-------|:-------------------|:--------------|:--------------------|:------------------|:-----------------|:---------------|:---------------|:--------|:---------------|:-------------------|:---------|:----------|:--------------|:--------------|
| 0 | 28 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | X | X | X | X | | X | | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/shimanto_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T22:43:56+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T22:54:36+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of shimanto/四万十/四万十 (Azur Lane)
=======================================
This is the dataset of shimanto/四万十/四万十 (Azur Lane), containing 40 images and their tags.
The core tags of this character are 'breasts, long\_hair, large\_breasts, red\_eyes, white\_hair, bangs, horns, very\_long\_hair, dragon\_girl', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
f1717ef6c29c85e677aeaf65fc79a58852354ee6 |
# Dataset of guichen/ギシャン/吉尚 (Azur Lane)
This is the dataset of guichen/ギシャン/吉尚 (Azur Lane), containing 22 images and their tags.
The core tags of this character are `long_hair, breasts, hat, large_breasts, white_headwear, witch_hat, earrings, bangs, purple_eyes, very_long_hair, blue_eyes, mole, white_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 22 | 38.86 MiB | [Download](https://huggingface.co/datasets/CyberHarem/guichen_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 22 | 18.84 MiB | [Download](https://huggingface.co/datasets/CyberHarem/guichen_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 52 | 39.55 MiB | [Download](https://huggingface.co/datasets/CyberHarem/guichen_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 22 | 32.81 MiB | [Download](https://huggingface.co/datasets/CyberHarem/guichen_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 52 | 62.81 MiB | [Download](https://huggingface.co/datasets/CyberHarem/guichen_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/guichen_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 22 |  |  |  |  |  | 1girl, bare_shoulders, solo, jewelry, looking_at_viewer, detached_sleeves, smile, white_dress, white_thighhighs, black_panties, crescent, thighs, navel, see-through, blush, witch |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | solo | jewelry | looking_at_viewer | detached_sleeves | smile | white_dress | white_thighhighs | black_panties | crescent | thighs | navel | see-through | blush | witch |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:-------|:----------|:--------------------|:-------------------|:--------|:--------------|:-------------------|:----------------|:-----------|:---------|:--------|:--------------|:--------|:--------|
| 0 | 22 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/guichen_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T22:44:06+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T22:49:32+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of guichen/ギシャン/吉尚 (Azur Lane)
======================================
This is the dataset of guichen/ギシャン/吉尚 (Azur Lane), containing 22 images and their tags.
The core tags of this character are 'long\_hair, breasts, hat, large\_breasts, white\_headwear, witch\_hat, earrings, bangs, purple\_eyes, very\_long\_hair, blue\_eyes, mole, white\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
d0868835f986803bc2e54dccb5d40a457f68810e |
# Dataset of allen_m_sumner/アレン・M・サムナー/艾伦·萨姆纳 (Azur Lane)
This is the dataset of allen_m_sumner/アレン・M・サムナー/艾伦·萨姆纳 (Azur Lane), containing 41 images and their tags.
The core tags of this character are `breasts, long_hair, red_eyes, black_hair, bangs, hair_between_eyes, twintails, hair_ornament, medium_breasts, very_long_hair, bow, large_breasts, animal_ears, blue_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 41 | 67.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/allen_m_sumner_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 41 | 35.12 MiB | [Download](https://huggingface.co/datasets/CyberHarem/allen_m_sumner_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 104 | 76.03 MiB | [Download](https://huggingface.co/datasets/CyberHarem/allen_m_sumner_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 41 | 57.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/allen_m_sumner_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 104 | 113.14 MiB | [Download](https://huggingface.co/datasets/CyberHarem/allen_m_sumner_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/allen_m_sumner_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 13 |  |  |  |  |  | 1girl, bare_shoulders, double_bun, off_shoulder, official_alternate_costume, playboy_bunny, rabbit_ears, solo, black_jacket, black_leotard, long_sleeves, looking_at_viewer, open_jacket, fake_animal_ears, smile, hair_bow, underboob_cutout, braided_bun, brown_pantyhose, sitting, ass, tongue_out, bodystocking, closed_mouth, simple_background, sleeves_past_wrists, black_footwear, blush, shoes, white_background |
| 1 | 18 |  |  |  |  |  | looking_at_viewer, underboob_cutout, 1girl, solo, bare_shoulders, two-tone_leotard, off_shoulder, open_coat, black_leotard, open_mouth, skindentation, black_coat, blush, groin, long_sleeves, thigh_strap, badge, cowboy_shot, frilled_leotard, standing, sidelocks, :d, armpits, ass_visible_through_thighs, white_leotard |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | double_bun | off_shoulder | official_alternate_costume | playboy_bunny | rabbit_ears | solo | black_jacket | black_leotard | long_sleeves | looking_at_viewer | open_jacket | fake_animal_ears | smile | hair_bow | underboob_cutout | braided_bun | brown_pantyhose | sitting | ass | tongue_out | bodystocking | closed_mouth | simple_background | sleeves_past_wrists | black_footwear | blush | shoes | white_background | two-tone_leotard | open_coat | open_mouth | skindentation | black_coat | groin | thigh_strap | badge | cowboy_shot | frilled_leotard | standing | sidelocks | :d | armpits | ass_visible_through_thighs | white_leotard |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:-------------|:---------------|:-----------------------------|:----------------|:--------------|:-------|:---------------|:----------------|:---------------|:--------------------|:--------------|:-------------------|:--------|:-----------|:-------------------|:--------------|:------------------|:----------|:------|:-------------|:---------------|:---------------|:--------------------|:----------------------|:-----------------|:--------|:--------|:-------------------|:-------------------|:------------|:-------------|:----------------|:-------------|:--------|:--------------|:--------|:--------------|:------------------|:-----------|:------------|:-----|:----------|:-----------------------------|:----------------|
| 0 | 13 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | |
| 1 | 18 |  |  |  |  |  | X | X | | X | | | | X | | X | X | X | | | | | X | | | | | | | | | | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/allen_m_sumner_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T22:44:12+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T22:56:02+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of allen\_m\_sumner/アレン・M・サムナー/艾伦·萨姆纳 (Azur Lane)
=========================================================
This is the dataset of allen\_m\_sumner/アレン・M・サムナー/艾伦·萨姆纳 (Azur Lane), containing 41 images and their tags.
The core tags of this character are 'breasts, long\_hair, red\_eyes, black\_hair, bangs, hair\_between\_eyes, twintails, hair\_ornament, medium\_breasts, very\_long\_hair, bow, large\_breasts, animal\_ears, blue\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
80e0179ebd1ccca27072d245dcd748c81400774e |
# Dataset of chiyoda/千代田/千代田 (Azur Lane)
This is the dataset of chiyoda/千代田/千代田 (Azur Lane), containing 31 images and their tags.
The core tags of this character are `breasts, red_hair, animal_ears, large_breasts, long_hair, purple_eyes, bangs, fox_ears, animal_ear_fluff, hair_ornament, hair_flower`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 31 | 62.54 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chiyoda_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 31 | 35.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chiyoda_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 81 | 73.65 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chiyoda_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 31 | 55.06 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chiyoda_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 81 | 111.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chiyoda_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/chiyoda_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 14 |  |  |  |  |  | looking_at_viewer, 1girl, red_bikini, flower, solo, smile, blush, cleavage, collar, navel, red_eyes, side-tie_bikini_bottom, string_bikini, choker, day, bare_shoulders, hair_between_eyes, open_mouth, outdoors, sky |
| 1 | 9 |  |  |  |  |  | 1girl, fox_mask, looking_at_viewer, solo, wide_sleeves, cleavage, mask_on_head, white_thighhighs, detached_sleeves, armpits, red_skirt, tongue_out, full_body, kimono, sash |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | looking_at_viewer | 1girl | red_bikini | flower | solo | smile | blush | cleavage | collar | navel | red_eyes | side-tie_bikini_bottom | string_bikini | choker | day | bare_shoulders | hair_between_eyes | open_mouth | outdoors | sky | fox_mask | wide_sleeves | mask_on_head | white_thighhighs | detached_sleeves | armpits | red_skirt | tongue_out | full_body | kimono | sash |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------|:--------|:-------------|:---------|:-------|:--------|:--------|:-----------|:---------|:--------|:-----------|:-------------------------|:----------------|:---------|:------|:-----------------|:--------------------|:-------------|:-----------|:------|:-----------|:---------------|:---------------|:-------------------|:-------------------|:----------|:------------|:-------------|:------------|:---------|:-------|
| 0 | 14 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | |
| 1 | 9 |  |  |  |  |  | X | X | | | X | | | X | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/chiyoda_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T22:44:12+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T22:54:32+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of chiyoda/千代田/千代田 (Azur Lane)
======================================
This is the dataset of chiyoda/千代田/千代田 (Azur Lane), containing 31 images and their tags.
The core tags of this character are 'breasts, red\_hair, animal\_ears, large\_breasts, long\_hair, purple\_eyes, bangs, fox\_ears, animal\_ear\_fluff, hair\_ornament, hair\_flower', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
86e6d0f7afbdac0c118edd98dc00f8f839b0073c |
# Dataset of an_shan/鞍山/鞍山 (Azur Lane)
This is the dataset of an_shan/鞍山/鞍山 (Azur Lane), containing 25 images and their tags.
The core tags of this character are `green_eyes, long_hair, green_hair, ponytail, hair_ornament, bangs, hairclip, braid, very_long_hair, breasts, hat, horns`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 25 | 28.23 MiB | [Download](https://huggingface.co/datasets/CyberHarem/an_shan_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 25 | 17.49 MiB | [Download](https://huggingface.co/datasets/CyberHarem/an_shan_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 56 | 35.68 MiB | [Download](https://huggingface.co/datasets/CyberHarem/an_shan_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 25 | 25.59 MiB | [Download](https://huggingface.co/datasets/CyberHarem/an_shan_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 56 | 49.14 MiB | [Download](https://huggingface.co/datasets/CyberHarem/an_shan_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/an_shan_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------|
| 0 | 25 |  |  |  |  |  | 1girl, blush, looking_at_viewer, solo, fingerless_gloves, epaulettes, black_gloves, long_sleeves, uniform |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | looking_at_viewer | solo | fingerless_gloves | epaulettes | black_gloves | long_sleeves | uniform |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:--------------------|:-------|:--------------------|:-------------|:---------------|:---------------|:----------|
| 0 | 25 |  |  |  |  |  | X | X | X | X | X | X | X | X | X |
| CyberHarem/an_shan_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T22:44:14+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T22:50:41+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of an\_shan/鞍山/鞍山 (Azur Lane)
=====================================
This is the dataset of an\_shan/鞍山/鞍山 (Azur Lane), containing 25 images and their tags.
The core tags of this character are 'green\_eyes, long\_hair, green\_hair, ponytail, hair\_ornament, bangs, hairclip, braid, very\_long\_hair, breasts, hat, horns', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
48e4b13db97c686c88adef324e3d1b8070212bd5 |
# Dataset of forbin/フォルバン/福尔班 (Azur Lane)
This is the dataset of forbin/フォルバン/福尔班 (Azur Lane), containing 36 images and their tags.
The core tags of this character are `blonde_hair, long_hair, breasts, green_eyes, braid, large_breasts, bangs, bow, hair_ornament, hair_bun, ribbon, single_hair_bun`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 36 | 45.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/forbin_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 36 | 27.08 MiB | [Download](https://huggingface.co/datasets/CyberHarem/forbin_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 84 | 55.68 MiB | [Download](https://huggingface.co/datasets/CyberHarem/forbin_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 36 | 40.90 MiB | [Download](https://huggingface.co/datasets/CyberHarem/forbin_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 84 | 76.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/forbin_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/forbin_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 23 |  |  |  |  |  | 1girl, blush, looking_at_viewer, solo, white_dress, bare_shoulders, cleavage, white_gloves, collarbone, elbow_gloves, fingerless_gloves, hair_bow, holding, open_mouth, white_bow, hair_between_eyes, parted_lips |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | looking_at_viewer | solo | white_dress | bare_shoulders | cleavage | white_gloves | collarbone | elbow_gloves | fingerless_gloves | hair_bow | holding | open_mouth | white_bow | hair_between_eyes | parted_lips |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:--------------------|:-------|:--------------|:-----------------|:-----------|:---------------|:-------------|:---------------|:--------------------|:-----------|:----------|:-------------|:------------|:--------------------|:--------------|
| 0 | 23 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/forbin_azurlane | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T22:44:16+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T22:53:13+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of forbin/フォルバン/福尔班 (Azur Lane)
=======================================
This is the dataset of forbin/フォルバン/福尔班 (Azur Lane), containing 36 images and their tags.
The core tags of this character are 'blonde\_hair, long\_hair, breasts, green\_eyes, braid, large\_breasts, bangs, bow, hair\_ornament, hair\_bun, ribbon, single\_hair\_bun', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
e98bd565f11ee405be251d09d536f9578a4c9252 |
This is a dataset of translation variants generated for `load_dataset("facebook/flores", "eng_Latn-ukr_Cyrl")["dev"]` using [mistralai/Mistral-7B-v0.1](https://docs.mistral.ai/self-deployment/vllm/).
Data was generated using the following script:
```python
import sys
import requests
import json
context = """[INST] They are planning to host a party next weekend. [/INST] Вони планують провести вечірку наступного вікенду.
[INST] I enjoy swimming in the ocean and feeling the salty breeze. [/INST] Мені подобається плавати в океані та відчувати солоний вітер.
[INST]"""
def prompt(input, url="http://localhost:8000/v1/completions"):
data = {
"prompt": f"{context} {input} [/INST]",
"stop": "[INST]",
"max_tokens": 512,
"temperature": 0,
#"temperature": 1.0,
#"top_p": 0.001,
#"top_k": 40,
"model": "mistralai/Mistral-7B-v0.1",
"presence_penalty": 0.1,
"use_beam_search": True,
"n": 25,
"logprobs": 1,
}
headers = {
"Content-Type": "application/json"
}
response = requests.post(url, headers=headers, data=json.dumps(data))
result = response.json()
return result
for line in sys.stdin:
text = prompt(line.strip())
print(json.dumps(text, ensure_ascii=False))
```
Quickly run vllm locally using:
```
docker run --gpus all -p 8000:8000 -e HF_HOME=/hf -e CUDA_VISIBLE_DEVICES=0 -v ~/.cache/huggingface:/hf \
ghcr.io/mistralai/mistral-src/vllm:latest --host 0.0.0.0 --model mistralai/Mistral-7B-v0.1
``` | darkproger/flores-uk-beams | [
"task_categories:translation",
"size_categories:n<1K",
"language:uk",
"language:en",
"license:mit",
"region:us"
] | 2024-01-13T22:48:23+00:00 | {"language": ["uk", "en"], "license": "mit", "size_categories": ["n<1K"], "task_categories": ["translation"]} | 2024-01-13T23:51:31+00:00 | [] | [
"uk",
"en"
] | TAGS
#task_categories-translation #size_categories-n<1K #language-Ukrainian #language-English #license-mit #region-us
|
This is a dataset of translation variants generated for 'load_dataset("facebook/flores", "eng_Latn-ukr_Cyrl")["dev"]' using mistralai/Mistral-7B-v0.1.
Data was generated using the following script:
Quickly run vllm locally using:
| [] | [
"TAGS\n#task_categories-translation #size_categories-n<1K #language-Ukrainian #language-English #license-mit #region-us \n"
] |
228a2cbad7e7d8961f98e258eef90686a778628d |
# Dataset Card for Evaluation run of SanjiWatsuki/Lelantos-DPO-7B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [SanjiWatsuki/Lelantos-DPO-7B](https://huggingface.co/SanjiWatsuki/Lelantos-DPO-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_SanjiWatsuki__Lelantos-DPO-7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T22:46:02.001551](https://huggingface.co/datasets/open-llm-leaderboard/details_SanjiWatsuki__Lelantos-DPO-7B/blob/main/results_2024-01-13T22-46-02.001551.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6448646713155067,
"acc_stderr": 0.03228864323452271,
"acc_norm": 0.6451783471312221,
"acc_norm_stderr": 0.03294673289821137,
"mc1": 0.5067319461444308,
"mc1_stderr": 0.017501914492655396,
"mc2": 0.6777342992399603,
"mc2_stderr": 0.01515297850307826
},
"harness|arc:challenge|25": {
"acc": 0.6732081911262798,
"acc_stderr": 0.013706665975587333,
"acc_norm": 0.7107508532423208,
"acc_norm_stderr": 0.013250012579393443
},
"harness|hellaswag|10": {
"acc": 0.6960764787890859,
"acc_stderr": 0.004590100050198808,
"acc_norm": 0.8722366062537343,
"acc_norm_stderr": 0.0033314391934060423
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5777777777777777,
"acc_stderr": 0.04266763404099582,
"acc_norm": 0.5777777777777777,
"acc_norm_stderr": 0.04266763404099582
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7171052631578947,
"acc_stderr": 0.03665349695640767,
"acc_norm": 0.7171052631578947,
"acc_norm_stderr": 0.03665349695640767
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.59,
"acc_stderr": 0.049431107042371025,
"acc_norm": 0.59,
"acc_norm_stderr": 0.049431107042371025
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6792452830188679,
"acc_stderr": 0.028727502957880274,
"acc_norm": 0.6792452830188679,
"acc_norm_stderr": 0.028727502957880274
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7430555555555556,
"acc_stderr": 0.03653946969442099,
"acc_norm": 0.7430555555555556,
"acc_norm_stderr": 0.03653946969442099
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.45,
"acc_stderr": 0.05,
"acc_norm": 0.45,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.5,
"acc_stderr": 0.050251890762960605,
"acc_norm": 0.5,
"acc_norm_stderr": 0.050251890762960605
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.29,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.29,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.630057803468208,
"acc_stderr": 0.0368122963339432,
"acc_norm": 0.630057803468208,
"acc_norm_stderr": 0.0368122963339432
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4117647058823529,
"acc_stderr": 0.048971049527263666,
"acc_norm": 0.4117647058823529,
"acc_norm_stderr": 0.048971049527263666
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5702127659574469,
"acc_stderr": 0.03236214467715564,
"acc_norm": 0.5702127659574469,
"acc_norm_stderr": 0.03236214467715564
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.543859649122807,
"acc_stderr": 0.04685473041907789,
"acc_norm": 0.543859649122807,
"acc_norm_stderr": 0.04685473041907789
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5448275862068965,
"acc_stderr": 0.04149886942192117,
"acc_norm": 0.5448275862068965,
"acc_norm_stderr": 0.04149886942192117
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.42857142857142855,
"acc_stderr": 0.02548718714785938,
"acc_norm": 0.42857142857142855,
"acc_norm_stderr": 0.02548718714785938
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.46825396825396826,
"acc_stderr": 0.04463112720677172,
"acc_norm": 0.46825396825396826,
"acc_norm_stderr": 0.04463112720677172
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.38,
"acc_stderr": 0.04878317312145632,
"acc_norm": 0.38,
"acc_norm_stderr": 0.04878317312145632
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7806451612903226,
"acc_stderr": 0.02354079935872329,
"acc_norm": 0.7806451612903226,
"acc_norm_stderr": 0.02354079935872329
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5221674876847291,
"acc_stderr": 0.03514528562175008,
"acc_norm": 0.5221674876847291,
"acc_norm_stderr": 0.03514528562175008
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.72,
"acc_stderr": 0.04512608598542127,
"acc_norm": 0.72,
"acc_norm_stderr": 0.04512608598542127
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7515151515151515,
"acc_stderr": 0.033744026441394036,
"acc_norm": 0.7515151515151515,
"acc_norm_stderr": 0.033744026441394036
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7727272727272727,
"acc_stderr": 0.029857515673386417,
"acc_norm": 0.7727272727272727,
"acc_norm_stderr": 0.029857515673386417
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8963730569948186,
"acc_stderr": 0.02199531196364424,
"acc_norm": 0.8963730569948186,
"acc_norm_stderr": 0.02199531196364424
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6307692307692307,
"acc_stderr": 0.02446861524147892,
"acc_norm": 0.6307692307692307,
"acc_norm_stderr": 0.02446861524147892
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3037037037037037,
"acc_stderr": 0.028037929969115,
"acc_norm": 0.3037037037037037,
"acc_norm_stderr": 0.028037929969115
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6848739495798319,
"acc_stderr": 0.030176808288974337,
"acc_norm": 0.6848739495798319,
"acc_norm_stderr": 0.030176808288974337
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.32450331125827814,
"acc_stderr": 0.03822746937658752,
"acc_norm": 0.32450331125827814,
"acc_norm_stderr": 0.03822746937658752
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8330275229357799,
"acc_stderr": 0.015990154885073403,
"acc_norm": 0.8330275229357799,
"acc_norm_stderr": 0.015990154885073403
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5138888888888888,
"acc_stderr": 0.03408655867977749,
"acc_norm": 0.5138888888888888,
"acc_norm_stderr": 0.03408655867977749
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8186274509803921,
"acc_stderr": 0.027044621719474082,
"acc_norm": 0.8186274509803921,
"acc_norm_stderr": 0.027044621719474082
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8143459915611815,
"acc_stderr": 0.025310495376944856,
"acc_norm": 0.8143459915611815,
"acc_norm_stderr": 0.025310495376944856
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6771300448430493,
"acc_stderr": 0.031381476375754995,
"acc_norm": 0.6771300448430493,
"acc_norm_stderr": 0.031381476375754995
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7862595419847328,
"acc_stderr": 0.0359546161177469,
"acc_norm": 0.7862595419847328,
"acc_norm_stderr": 0.0359546161177469
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7851239669421488,
"acc_stderr": 0.037494924487096966,
"acc_norm": 0.7851239669421488,
"acc_norm_stderr": 0.037494924487096966
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.75,
"acc_stderr": 0.04186091791394607,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04186091791394607
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7914110429447853,
"acc_stderr": 0.031921934489347235,
"acc_norm": 0.7914110429447853,
"acc_norm_stderr": 0.031921934489347235
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.48214285714285715,
"acc_stderr": 0.047427623612430116,
"acc_norm": 0.48214285714285715,
"acc_norm_stderr": 0.047427623612430116
},
"harness|hendrycksTest-management|5": {
"acc": 0.7864077669902912,
"acc_stderr": 0.040580420156460344,
"acc_norm": 0.7864077669902912,
"acc_norm_stderr": 0.040580420156460344
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8675213675213675,
"acc_stderr": 0.022209309073165612,
"acc_norm": 0.8675213675213675,
"acc_norm_stderr": 0.022209309073165612
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.69,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8263090676883781,
"acc_stderr": 0.01354741565866226,
"acc_norm": 0.8263090676883781,
"acc_norm_stderr": 0.01354741565866226
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7052023121387283,
"acc_stderr": 0.024547617794803828,
"acc_norm": 0.7052023121387283,
"acc_norm_stderr": 0.024547617794803828
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.4011173184357542,
"acc_stderr": 0.01639222189940707,
"acc_norm": 0.4011173184357542,
"acc_norm_stderr": 0.01639222189940707
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7222222222222222,
"acc_stderr": 0.025646863097137897,
"acc_norm": 0.7222222222222222,
"acc_norm_stderr": 0.025646863097137897
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7202572347266881,
"acc_stderr": 0.025494259350694912,
"acc_norm": 0.7202572347266881,
"acc_norm_stderr": 0.025494259350694912
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7314814814814815,
"acc_stderr": 0.024659685185967284,
"acc_norm": 0.7314814814814815,
"acc_norm_stderr": 0.024659685185967284
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.5070921985815603,
"acc_stderr": 0.02982449855912901,
"acc_norm": 0.5070921985815603,
"acc_norm_stderr": 0.02982449855912901
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4784876140808344,
"acc_stderr": 0.012758410941038911,
"acc_norm": 0.4784876140808344,
"acc_norm_stderr": 0.012758410941038911
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.7058823529411765,
"acc_stderr": 0.027678468642144717,
"acc_norm": 0.7058823529411765,
"acc_norm_stderr": 0.027678468642144717
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.673202614379085,
"acc_stderr": 0.018975427920507208,
"acc_norm": 0.673202614379085,
"acc_norm_stderr": 0.018975427920507208
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6909090909090909,
"acc_stderr": 0.044262946482000985,
"acc_norm": 0.6909090909090909,
"acc_norm_stderr": 0.044262946482000985
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7387755102040816,
"acc_stderr": 0.02812342933514278,
"acc_norm": 0.7387755102040816,
"acc_norm_stderr": 0.02812342933514278
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8109452736318408,
"acc_stderr": 0.02768691358801301,
"acc_norm": 0.8109452736318408,
"acc_norm_stderr": 0.02768691358801301
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.84,
"acc_stderr": 0.03684529491774708,
"acc_norm": 0.84,
"acc_norm_stderr": 0.03684529491774708
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5481927710843374,
"acc_stderr": 0.03874371556587953,
"acc_norm": 0.5481927710843374,
"acc_norm_stderr": 0.03874371556587953
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8245614035087719,
"acc_stderr": 0.029170885500727665,
"acc_norm": 0.8245614035087719,
"acc_norm_stderr": 0.029170885500727665
},
"harness|truthfulqa:mc|0": {
"mc1": 0.5067319461444308,
"mc1_stderr": 0.017501914492655396,
"mc2": 0.6777342992399603,
"mc2_stderr": 0.01515297850307826
},
"harness|winogrande|5": {
"acc": 0.8003157063930545,
"acc_stderr": 0.01123532838262585
},
"harness|gsm8k|5": {
"acc": 0.6846095526914329,
"acc_stderr": 0.01279935367580183
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## Dataset Structure
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Who are the source data producers?
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### Annotations [optional]
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#### Annotation process
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#### Who are the annotators?
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#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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## Bias, Risks, and Limitations
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## Citation [optional]
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## Glossary [optional]
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_SanjiWatsuki__Lelantos-DPO-7B | [
"region:us"
] | 2024-01-13T22:48:23+00:00 | {"pretty_name": "Evaluation run of SanjiWatsuki/Lelantos-DPO-7B", "dataset_summary": "Dataset automatically created during the evaluation run of model [SanjiWatsuki/Lelantos-DPO-7B](https://huggingface.co/SanjiWatsuki/Lelantos-DPO-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_SanjiWatsuki__Lelantos-DPO-7B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T22:46:02.001551](https://huggingface.co/datasets/open-llm-leaderboard/details_SanjiWatsuki__Lelantos-DPO-7B/blob/main/results_2024-01-13T22-46-02.001551.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6448646713155067,\n \"acc_stderr\": 0.03228864323452271,\n \"acc_norm\": 0.6451783471312221,\n \"acc_norm_stderr\": 0.03294673289821137,\n \"mc1\": 0.5067319461444308,\n \"mc1_stderr\": 0.017501914492655396,\n \"mc2\": 0.6777342992399603,\n \"mc2_stderr\": 0.01515297850307826\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6732081911262798,\n \"acc_stderr\": 0.013706665975587333,\n \"acc_norm\": 0.7107508532423208,\n \"acc_norm_stderr\": 0.013250012579393443\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6960764787890859,\n \"acc_stderr\": 0.004590100050198808,\n \"acc_norm\": 0.8722366062537343,\n \"acc_norm_stderr\": 0.0033314391934060423\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5777777777777777,\n \"acc_stderr\": 0.04266763404099582,\n \"acc_norm\": 0.5777777777777777,\n \"acc_norm_stderr\": 0.04266763404099582\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.7171052631578947,\n \"acc_stderr\": 0.03665349695640767,\n \"acc_norm\": 0.7171052631578947,\n \"acc_norm_stderr\": 0.03665349695640767\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.59,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6792452830188679,\n \"acc_stderr\": 0.028727502957880274,\n \"acc_norm\": 0.6792452830188679,\n \"acc_norm_stderr\": 0.028727502957880274\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7430555555555556,\n \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.7430555555555556,\n \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.630057803468208,\n \"acc_stderr\": 0.0368122963339432,\n \"acc_norm\": 0.630057803468208,\n \"acc_norm_stderr\": 0.0368122963339432\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.048971049527263666,\n \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.048971049527263666\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5702127659574469,\n \"acc_stderr\": 0.03236214467715564,\n \"acc_norm\": 0.5702127659574469,\n \"acc_norm_stderr\": 0.03236214467715564\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.543859649122807,\n \"acc_stderr\": 0.04685473041907789,\n \"acc_norm\": 0.543859649122807,\n \"acc_norm_stderr\": 0.04685473041907789\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192117,\n \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192117\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.42857142857142855,\n \"acc_stderr\": 0.02548718714785938,\n \"acc_norm\": 0.42857142857142855,\n \"acc_norm_stderr\": 0.02548718714785938\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.46825396825396826,\n \"acc_stderr\": 0.04463112720677172,\n \"acc_norm\": 0.46825396825396826,\n \"acc_norm_stderr\": 0.04463112720677172\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7806451612903226,\n \"acc_stderr\": 0.02354079935872329,\n \"acc_norm\": 0.7806451612903226,\n \"acc_norm_stderr\": 0.02354079935872329\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5221674876847291,\n \"acc_stderr\": 0.03514528562175008,\n \"acc_norm\": 0.5221674876847291,\n \"acc_norm_stderr\": 0.03514528562175008\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7515151515151515,\n \"acc_stderr\": 0.033744026441394036,\n \"acc_norm\": 0.7515151515151515,\n \"acc_norm_stderr\": 0.033744026441394036\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7727272727272727,\n \"acc_stderr\": 0.029857515673386417,\n \"acc_norm\": 0.7727272727272727,\n \"acc_norm_stderr\": 0.029857515673386417\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.02199531196364424,\n \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.02199531196364424\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6307692307692307,\n \"acc_stderr\": 0.02446861524147892,\n \"acc_norm\": 0.6307692307692307,\n \"acc_norm_stderr\": 0.02446861524147892\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.3037037037037037,\n \"acc_stderr\": 0.028037929969115,\n \"acc_norm\": 0.3037037037037037,\n \"acc_norm_stderr\": 0.028037929969115\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6848739495798319,\n \"acc_stderr\": 0.030176808288974337,\n \"acc_norm\": 0.6848739495798319,\n \"acc_norm_stderr\": 0.030176808288974337\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.32450331125827814,\n \"acc_stderr\": 0.03822746937658752,\n \"acc_norm\": 0.32450331125827814,\n \"acc_norm_stderr\": 0.03822746937658752\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8330275229357799,\n \"acc_stderr\": 0.015990154885073403,\n \"acc_norm\": 0.8330275229357799,\n \"acc_norm_stderr\": 0.015990154885073403\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5138888888888888,\n \"acc_stderr\": 0.03408655867977749,\n \"acc_norm\": 0.5138888888888888,\n \"acc_norm_stderr\": 0.03408655867977749\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8186274509803921,\n \"acc_stderr\": 0.027044621719474082,\n \"acc_norm\": 0.8186274509803921,\n \"acc_norm_stderr\": 0.027044621719474082\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.8143459915611815,\n \"acc_stderr\": 0.025310495376944856,\n \"acc_norm\": 0.8143459915611815,\n \"acc_norm_stderr\": 0.025310495376944856\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6771300448430493,\n \"acc_stderr\": 0.031381476375754995,\n \"acc_norm\": 0.6771300448430493,\n \"acc_norm_stderr\": 0.031381476375754995\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.7862595419847328,\n \"acc_stderr\": 0.0359546161177469,\n \"acc_norm\": 0.7862595419847328,\n \"acc_norm_stderr\": 0.0359546161177469\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7914110429447853,\n \"acc_stderr\": 0.031921934489347235,\n \"acc_norm\": 0.7914110429447853,\n \"acc_norm_stderr\": 0.031921934489347235\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.48214285714285715,\n \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.48214285714285715,\n \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8675213675213675,\n \"acc_stderr\": 0.022209309073165612,\n \"acc_norm\": 0.8675213675213675,\n \"acc_norm_stderr\": 0.022209309073165612\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8263090676883781,\n \"acc_stderr\": 0.01354741565866226,\n \"acc_norm\": 0.8263090676883781,\n \"acc_norm_stderr\": 0.01354741565866226\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7052023121387283,\n \"acc_stderr\": 0.024547617794803828,\n \"acc_norm\": 0.7052023121387283,\n \"acc_norm_stderr\": 0.024547617794803828\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4011173184357542,\n \"acc_stderr\": 0.01639222189940707,\n \"acc_norm\": 0.4011173184357542,\n \"acc_norm_stderr\": 0.01639222189940707\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.025646863097137897,\n \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.025646863097137897\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7202572347266881,\n \"acc_stderr\": 0.025494259350694912,\n \"acc_norm\": 0.7202572347266881,\n \"acc_norm_stderr\": 0.025494259350694912\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.7314814814814815,\n \"acc_stderr\": 0.024659685185967284,\n \"acc_norm\": 0.7314814814814815,\n \"acc_norm_stderr\": 0.024659685185967284\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.5070921985815603,\n \"acc_stderr\": 0.02982449855912901,\n \"acc_norm\": 0.5070921985815603,\n \"acc_norm_stderr\": 0.02982449855912901\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4784876140808344,\n \"acc_stderr\": 0.012758410941038911,\n \"acc_norm\": 0.4784876140808344,\n \"acc_norm_stderr\": 0.012758410941038911\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.7058823529411765,\n \"acc_stderr\": 0.027678468642144717,\n \"acc_norm\": 0.7058823529411765,\n \"acc_norm_stderr\": 0.027678468642144717\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.673202614379085,\n \"acc_stderr\": 0.018975427920507208,\n \"acc_norm\": 0.673202614379085,\n \"acc_norm_stderr\": 0.018975427920507208\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6909090909090909,\n \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.6909090909090909,\n \"acc_norm_stderr\": 0.044262946482000985\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.7387755102040816,\n \"acc_stderr\": 0.02812342933514278,\n \"acc_norm\": 0.7387755102040816,\n \"acc_norm_stderr\": 0.02812342933514278\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8109452736318408,\n \"acc_stderr\": 0.02768691358801301,\n \"acc_norm\": 0.8109452736318408,\n \"acc_norm_stderr\": 0.02768691358801301\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774708,\n \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774708\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5481927710843374,\n \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.8245614035087719,\n \"acc_stderr\": 0.029170885500727665,\n \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.029170885500727665\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5067319461444308,\n \"mc1_stderr\": 0.017501914492655396,\n \"mc2\": 0.6777342992399603,\n \"mc2_stderr\": 0.01515297850307826\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.8003157063930545,\n \"acc_stderr\": 0.01123532838262585\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6846095526914329,\n \"acc_stderr\": 0.01279935367580183\n }\n}\n```", "repo_url": "https://huggingface.co/SanjiWatsuki/Lelantos-DPO-7B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2024_01_13T22_46_02.001551", "path": ["**/details_harness|arc:challenge|25_2024-01-13T22-46-02.001551.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-01-13T22-46-02.001551.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_01_13T22_46_02.001551", "path": ["**/details_harness|gsm8k|5_2024-01-13T22-46-02.001551.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-01-13T22-46-02.001551.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_01_13T22_46_02.001551", "path": ["**/details_harness|hellaswag|10_2024-01-13T22-46-02.001551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-01-13T22-46-02.001551.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_01_13T22_46_02.001551", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T22-46-02.001551.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-01-13T22-46-02.001551.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-01-13T22-46-02.001551.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T22-46-02.001551.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T22-46-02.001551.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-01-13T22-46-02.001551.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T22-46-02.001551.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T22-46-02.001551.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T22-46-02.001551.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T22-46-02.001551.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-01-13T22-46-02.001551.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-01-13T22-46-02.001551.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T22-46-02.001551.parquet", "**/details_harness|hendrycksTest-econometrics|5_2024-01-13T22-46-02.001551.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T22-46-02.001551.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T22-46-02.001551.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T22-46-02.001551.parquet", "**/details_harness|hendrycksTest-global_facts|5_2024-01-13T22-46-02.001551.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T22-46-02.001551.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T22-46-02.001551.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T22-46-02.001551.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-13T22-46-02.001551.parquet", "**/details_harness|hendrycksTest-high_school_geography|5_2024-01-13T22-46-02.001551.parquet", "**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-13T22-46-02.001551.parquet", "**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-13T22-46-02.001551.parquet", "**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-13T22-46-02.001551.parquet", "**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-13T22-46-02.001551.parquet", "**/details_harness|hendrycksTest-high_school_physics|5_2024-01-13T22-46-02.001551.parquet", "**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-13T22-46-02.001551.parquet", "**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-13T22-46-02.001551.parquet", "**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-13T22-46-02.001551.parquet", "**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T22-46-02.001551.parquet", 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["**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T22-46-02.001551.parquet"]}]}, {"config_name": "harness_hendrycksTest_international_law_5", "data_files": [{"split": "2024_01_13T22_46_02.001551", "path": ["**/details_harness|hendrycksTest-international_law|5_2024-01-13T22-46-02.001551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-international_law|5_2024-01-13T22-46-02.001551.parquet"]}]}, {"config_name": "harness_hendrycksTest_jurisprudence_5", "data_files": [{"split": "2024_01_13T22_46_02.001551", "path": ["**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T22-46-02.001551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T22-46-02.001551.parquet"]}]}, {"config_name": "harness_hendrycksTest_logical_fallacies_5", "data_files": [{"split": "2024_01_13T22_46_02.001551", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T22-46-02.001551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T22-46-02.001551.parquet"]}]}, {"config_name": "harness_hendrycksTest_machine_learning_5", "data_files": [{"split": "2024_01_13T22_46_02.001551", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T22-46-02.001551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T22-46-02.001551.parquet"]}]}, {"config_name": "harness_hendrycksTest_management_5", "data_files": [{"split": "2024_01_13T22_46_02.001551", "path": ["**/details_harness|hendrycksTest-management|5_2024-01-13T22-46-02.001551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-management|5_2024-01-13T22-46-02.001551.parquet"]}]}, {"config_name": "harness_hendrycksTest_marketing_5", "data_files": [{"split": "2024_01_13T22_46_02.001551", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-13T22-46-02.001551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-13T22-46-02.001551.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2024_01_13T22_46_02.001551", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T22-46-02.001551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T22-46-02.001551.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2024_01_13T22_46_02.001551", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T22-46-02.001551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T22-46-02.001551.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2024_01_13T22_46_02.001551", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T22-46-02.001551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T22-46-02.001551.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2024_01_13T22_46_02.001551", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T22-46-02.001551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T22-46-02.001551.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2024_01_13T22_46_02.001551", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T22-46-02.001551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T22-46-02.001551.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2024_01_13T22_46_02.001551", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T22-46-02.001551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T22-46-02.001551.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2024_01_13T22_46_02.001551", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T22-46-02.001551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T22-46-02.001551.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2024_01_13T22_46_02.001551", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T22-46-02.001551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T22-46-02.001551.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2024_01_13T22_46_02.001551", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T22-46-02.001551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T22-46-02.001551.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_01_13T22_46_02.001551", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T22-46-02.001551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T22-46-02.001551.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_01_13T22_46_02.001551", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T22-46-02.001551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T22-46-02.001551.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_01_13T22_46_02.001551", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T22-46-02.001551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T22-46-02.001551.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_01_13T22_46_02.001551", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T22-46-02.001551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T22-46-02.001551.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_01_13T22_46_02.001551", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T22-46-02.001551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T22-46-02.001551.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_01_13T22_46_02.001551", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T22-46-02.001551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T22-46-02.001551.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_13T22_46_02.001551", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T22-46-02.001551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T22-46-02.001551.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_13T22_46_02.001551", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T22-46-02.001551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T22-46-02.001551.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_13T22_46_02.001551", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T22-46-02.001551.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T22-46-02.001551.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T22_46_02.001551", "path": ["**/details_harness|winogrande|5_2024-01-13T22-46-02.001551.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T22-46-02.001551.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T22_46_02.001551", "path": ["results_2024-01-13T22-46-02.001551.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T22-46-02.001551.parquet"]}]}]} | 2024-01-13T22:48:44+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of SanjiWatsuki/Lelantos-DPO-7B
Dataset automatically created during the evaluation run of model SanjiWatsuki/Lelantos-DPO-7B on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-13T22:46:02.001551(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of SanjiWatsuki/Lelantos-DPO-7B\n\n\n\nDataset automatically created during the evaluation run of model SanjiWatsuki/Lelantos-DPO-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T22:46:02.001551(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of SanjiWatsuki/Lelantos-DPO-7B\n\n\n\nDataset automatically created during the evaluation run of model SanjiWatsuki/Lelantos-DPO-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T22:46:02.001551(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
9cf0db6fcbc68d7535b4e461a95f969f10777f55 |
# Dataset Card for Evaluation run of vicgalle/SOLAR-13B-Instruct-v1.0
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [vicgalle/SOLAR-13B-Instruct-v1.0](https://huggingface.co/vicgalle/SOLAR-13B-Instruct-v1.0) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_vicgalle__SOLAR-13B-Instruct-v1.0",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T23:03:16.622437](https://huggingface.co/datasets/open-llm-leaderboard/details_vicgalle__SOLAR-13B-Instruct-v1.0/blob/main/results_2024-01-13T23-03-16.622437.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.5538159165724174,
"acc_stderr": 0.03403197325352318,
"acc_norm": 0.5615645038041155,
"acc_norm_stderr": 0.03477929396757003,
"mc1": 0.44920440636474906,
"mc1_stderr": 0.01741294198611531,
"mc2": 0.619920564120794,
"mc2_stderr": 0.01593484036504592
},
"harness|arc:challenge|25": {
"acc": 0.5435153583617748,
"acc_stderr": 0.01455594976049644,
"acc_norm": 0.5725255972696246,
"acc_norm_stderr": 0.014456862944650647
},
"harness|hellaswag|10": {
"acc": 0.5913164708225453,
"acc_stderr": 0.004905859114942291,
"acc_norm": 0.7803226448914559,
"acc_norm_stderr": 0.004131818797713876
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.34,
"acc_stderr": 0.047609522856952365,
"acc_norm": 0.34,
"acc_norm_stderr": 0.047609522856952365
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.4962962962962963,
"acc_stderr": 0.04319223625811331,
"acc_norm": 0.4962962962962963,
"acc_norm_stderr": 0.04319223625811331
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6052631578947368,
"acc_stderr": 0.039777499346220734,
"acc_norm": 0.6052631578947368,
"acc_norm_stderr": 0.039777499346220734
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.62,
"acc_stderr": 0.048783173121456316,
"acc_norm": 0.62,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6037735849056604,
"acc_stderr": 0.030102793781791194,
"acc_norm": 0.6037735849056604,
"acc_norm_stderr": 0.030102793781791194
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.5694444444444444,
"acc_stderr": 0.04140685639111502,
"acc_norm": 0.5694444444444444,
"acc_norm_stderr": 0.04140685639111502
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.44,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.44,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.43,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.43,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5722543352601156,
"acc_stderr": 0.03772446857518026,
"acc_norm": 0.5722543352601156,
"acc_norm_stderr": 0.03772446857518026
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4215686274509804,
"acc_stderr": 0.04913595201274498,
"acc_norm": 0.4215686274509804,
"acc_norm_stderr": 0.04913595201274498
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.7,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.4808510638297872,
"acc_stderr": 0.03266204299064678,
"acc_norm": 0.4808510638297872,
"acc_norm_stderr": 0.03266204299064678
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.3508771929824561,
"acc_stderr": 0.044895393502706986,
"acc_norm": 0.3508771929824561,
"acc_norm_stderr": 0.044895393502706986
},
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}
}
```
## Dataset Details
### Dataset Description
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- **Curated by:** [More Information Needed]
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## Dataset Structure
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## Dataset Creation
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#### Data Collection and Processing
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#### Annotation process
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#### Personal and Sensitive Information
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_vicgalle__SOLAR-13B-Instruct-v1.0 | [
"region:us"
] | 2024-01-13T23:05:33+00:00 | {"pretty_name": "Evaluation run of vicgalle/SOLAR-13B-Instruct-v1.0", "dataset_summary": "Dataset automatically created during the evaluation run of model [vicgalle/SOLAR-13B-Instruct-v1.0](https://huggingface.co/vicgalle/SOLAR-13B-Instruct-v1.0) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_vicgalle__SOLAR-13B-Instruct-v1.0\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T23:03:16.622437](https://huggingface.co/datasets/open-llm-leaderboard/details_vicgalle__SOLAR-13B-Instruct-v1.0/blob/main/results_2024-01-13T23-03-16.622437.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.5538159165724174,\n \"acc_stderr\": 0.03403197325352318,\n \"acc_norm\": 0.5615645038041155,\n \"acc_norm_stderr\": 0.03477929396757003,\n \"mc1\": 0.44920440636474906,\n \"mc1_stderr\": 0.01741294198611531,\n \"mc2\": 0.619920564120794,\n \"mc2_stderr\": 0.01593484036504592\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5435153583617748,\n \"acc_stderr\": 0.01455594976049644,\n \"acc_norm\": 0.5725255972696246,\n \"acc_norm_stderr\": 0.014456862944650647\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5913164708225453,\n \"acc_stderr\": 0.004905859114942291,\n \"acc_norm\": 0.7803226448914559,\n \"acc_norm_stderr\": 0.004131818797713876\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.047609522856952365,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.047609522856952365\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4962962962962963,\n \"acc_stderr\": 0.04319223625811331,\n \"acc_norm\": 0.4962962962962963,\n \"acc_norm_stderr\": 0.04319223625811331\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6052631578947368,\n \"acc_stderr\": 0.039777499346220734,\n \"acc_norm\": 0.6052631578947368,\n \"acc_norm_stderr\": 0.039777499346220734\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.62,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.62,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6037735849056604,\n \"acc_stderr\": 0.030102793781791194,\n \"acc_norm\": 0.6037735849056604,\n \"acc_norm_stderr\": 0.030102793781791194\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5694444444444444,\n \"acc_stderr\": 0.04140685639111502,\n \"acc_norm\": 0.5694444444444444,\n \"acc_norm_stderr\": 0.04140685639111502\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5722543352601156,\n \"acc_stderr\": 0.03772446857518026,\n \"acc_norm\": 0.5722543352601156,\n \"acc_norm_stderr\": 0.03772446857518026\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.4808510638297872,\n \"acc_stderr\": 0.03266204299064678,\n \"acc_norm\": 0.4808510638297872,\n \"acc_norm_stderr\": 0.03266204299064678\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.3508771929824561,\n \"acc_stderr\": 0.044895393502706986,\n \"acc_norm\": 0.3508771929824561,\n \"acc_norm_stderr\": 0.044895393502706986\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878151,\n \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878151\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.36243386243386244,\n \"acc_stderr\": 0.024757473902752042,\n \"acc_norm\": 0.36243386243386244,\n \"acc_norm_stderr\": 0.024757473902752042\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3492063492063492,\n \"acc_stderr\": 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"path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T23-03-16.622437.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2024_01_13T23_03_16.622437", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T23-03-16.622437.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T23-03-16.622437.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2024_01_13T23_03_16.622437", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T23-03-16.622437.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T23-03-16.622437.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2024_01_13T23_03_16.622437", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T23-03-16.622437.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T23-03-16.622437.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2024_01_13T23_03_16.622437", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T23-03-16.622437.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T23-03-16.622437.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2024_01_13T23_03_16.622437", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T23-03-16.622437.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T23-03-16.622437.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2024_01_13T23_03_16.622437", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T23-03-16.622437.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T23-03-16.622437.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_01_13T23_03_16.622437", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T23-03-16.622437.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T23-03-16.622437.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_01_13T23_03_16.622437", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T23-03-16.622437.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T23-03-16.622437.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_01_13T23_03_16.622437", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T23-03-16.622437.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T23-03-16.622437.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_01_13T23_03_16.622437", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T23-03-16.622437.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T23-03-16.622437.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_01_13T23_03_16.622437", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T23-03-16.622437.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T23-03-16.622437.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_01_13T23_03_16.622437", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T23-03-16.622437.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T23-03-16.622437.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_13T23_03_16.622437", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T23-03-16.622437.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T23-03-16.622437.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_13T23_03_16.622437", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T23-03-16.622437.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T23-03-16.622437.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_13T23_03_16.622437", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T23-03-16.622437.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T23-03-16.622437.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T23_03_16.622437", "path": ["**/details_harness|winogrande|5_2024-01-13T23-03-16.622437.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T23-03-16.622437.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T23_03_16.622437", "path": ["results_2024-01-13T23-03-16.622437.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T23-03-16.622437.parquet"]}]}]} | 2024-01-13T23:05:54+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of vicgalle/SOLAR-13B-Instruct-v1.0
Dataset automatically created during the evaluation run of model vicgalle/SOLAR-13B-Instruct-v1.0 on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-13T23:03:16.622437(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of vicgalle/SOLAR-13B-Instruct-v1.0\n\n\n\nDataset automatically created during the evaluation run of model vicgalle/SOLAR-13B-Instruct-v1.0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T23:03:16.622437(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of vicgalle/SOLAR-13B-Instruct-v1.0\n\n\n\nDataset automatically created during the evaluation run of model vicgalle/SOLAR-13B-Instruct-v1.0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T23:03:16.622437(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
9a06e685c89286eb2bb8026411d150532f86ccb3 | # IndirectRequests
IndirectRequests is an LLM-generated dataset of user utterances in a task-oriented dialogue setting where the user does not directly specify their preferred slot value.
IndirectRequests was generated by crowdsourcing human labels over a dataset generated using a combination of GPT-3.5 (turbo) and GPT-4.
Each utterance is labelled along two dimensions:
1. World Understanding (the degree of world understanding it takes to understand the utterance)
2. Unambiguity (whether or not the generated utterance unambiguously entails a single target slot value among a set of candidate possible values).
---
license: mit
size_categories:
- n<1K
task_categories:
- text-classification
- conversational
- text-generation
pretty_name: IndirectRequests
configs:
- config_name: target_slot_value
data_files:
- split: train
path: data/train_target_slot_value.jsonl
- split: validation
path: data/validation_target_slot_value.jsonl
- split: test
path: data/test_target_slot_value.jsonl
- config_name: mean_world_understanding
data_files:
- split: train
path: data/train_mean_world_understanding.jsonl
- split: validation
path: data/validation_mean_world_understanding.jsonl
- split: test
path: data/test_mean_world_understanding.jsonl
---
| msamogh/indirect-requests | [
"task_categories:text-classification",
"task_categories:text-generation",
"task_categories:conversational",
"size_categories:n<1K",
"language:en",
"license:apache-2.0",
"region:us"
] | 2024-01-13T23:06:21+00:00 | {"language": ["en"], "license": "apache-2.0", "size_categories": ["n<1K"], "task_categories": ["text-classification", "text-generation", "conversational"], "pretty_name": "IIU-ToD"} | 2024-02-02T06:23:35+00:00 | [] | [
"en"
] | TAGS
#task_categories-text-classification #task_categories-text-generation #task_categories-conversational #size_categories-n<1K #language-English #license-apache-2.0 #region-us
| # IndirectRequests
IndirectRequests is an LLM-generated dataset of user utterances in a task-oriented dialogue setting where the user does not directly specify their preferred slot value.
IndirectRequests was generated by crowdsourcing human labels over a dataset generated using a combination of GPT-3.5 (turbo) and GPT-4.
Each utterance is labelled along two dimensions:
1. World Understanding (the degree of world understanding it takes to understand the utterance)
2. Unambiguity (whether or not the generated utterance unambiguously entails a single target slot value among a set of candidate possible values).
---
license: mit
size_categories:
- n<1K
task_categories:
- text-classification
- conversational
- text-generation
pretty_name: IndirectRequests
configs:
- config_name: target_slot_value
data_files:
- split: train
path: data/train_target_slot_value.jsonl
- split: validation
path: data/validation_target_slot_value.jsonl
- split: test
path: data/test_target_slot_value.jsonl
- config_name: mean_world_understanding
data_files:
- split: train
path: data/train_mean_world_understanding.jsonl
- split: validation
path: data/validation_mean_world_understanding.jsonl
- split: test
path: data/test_mean_world_understanding.jsonl
---
| [
"# IndirectRequests\n\nIndirectRequests is an LLM-generated dataset of user utterances in a task-oriented dialogue setting where the user does not directly specify their preferred slot value.\n\nIndirectRequests was generated by crowdsourcing human labels over a dataset generated using a combination of GPT-3.5 (turbo) and GPT-4.\nEach utterance is labelled along two dimensions:\n1. World Understanding (the degree of world understanding it takes to understand the utterance)\n2. Unambiguity (whether or not the generated utterance unambiguously entails a single target slot value among a set of candidate possible values).\n\n---\nlicense: mit\nsize_categories:\n- n<1K\ntask_categories:\n- text-classification\n- conversational\n- text-generation\npretty_name: IndirectRequests\nconfigs:\n- config_name: target_slot_value\n data_files:\n - split: train\n path: data/train_target_slot_value.jsonl\n - split: validation\n path: data/validation_target_slot_value.jsonl\n - split: test\n path: data/test_target_slot_value.jsonl\n- config_name: mean_world_understanding\n data_files:\n - split: train\n path: data/train_mean_world_understanding.jsonl\n - split: validation\n path: data/validation_mean_world_understanding.jsonl\n - split: test\n path: data/test_mean_world_understanding.jsonl\n---"
] | [
"TAGS\n#task_categories-text-classification #task_categories-text-generation #task_categories-conversational #size_categories-n<1K #language-English #license-apache-2.0 #region-us \n",
"# IndirectRequests\n\nIndirectRequests is an LLM-generated dataset of user utterances in a task-oriented dialogue setting where the user does not directly specify their preferred slot value.\n\nIndirectRequests was generated by crowdsourcing human labels over a dataset generated using a combination of GPT-3.5 (turbo) and GPT-4.\nEach utterance is labelled along two dimensions:\n1. World Understanding (the degree of world understanding it takes to understand the utterance)\n2. Unambiguity (whether or not the generated utterance unambiguously entails a single target slot value among a set of candidate possible values).\n\n---\nlicense: mit\nsize_categories:\n- n<1K\ntask_categories:\n- text-classification\n- conversational\n- text-generation\npretty_name: IndirectRequests\nconfigs:\n- config_name: target_slot_value\n data_files:\n - split: train\n path: data/train_target_slot_value.jsonl\n - split: validation\n path: data/validation_target_slot_value.jsonl\n - split: test\n path: data/test_target_slot_value.jsonl\n- config_name: mean_world_understanding\n data_files:\n - split: train\n path: data/train_mean_world_understanding.jsonl\n - split: validation\n path: data/validation_mean_world_understanding.jsonl\n - split: test\n path: data/test_mean_world_understanding.jsonl\n---"
] |
f80ca395f174a2333ef5db46a217954492b010c7 |
# Dataset Card for Evaluation run of TeeZee/2xbagel-dpo-34b-v0.2
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [TeeZee/2xbagel-dpo-34b-v0.2](https://huggingface.co/TeeZee/2xbagel-dpo-34b-v0.2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_TeeZee__2xbagel-dpo-34b-v0.2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T23:15:59.619735](https://huggingface.co/datasets/open-llm-leaderboard/details_TeeZee__2xbagel-dpo-34b-v0.2/blob/main/results_2024-01-13T23-15-59.619735.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.7214725397684685,
"acc_stderr": 0.029456464928054458,
"acc_norm": 0.7359963920471002,
"acc_norm_stderr": 0.030168902390549673,
"mc1": 0.5018359853121175,
"mc1_stderr": 0.017503383046877048,
"mc2": 0.6715187545754473,
"mc2_stderr": 0.015523811623029661
},
"harness|arc:challenge|25": {
"acc": 0.6356655290102389,
"acc_stderr": 0.014063260279882417,
"acc_norm": 0.6527303754266212,
"acc_norm_stderr": 0.013913034529620458
},
"harness|hellaswag|10": {
"acc": 0.6113324039036049,
"acc_stderr": 0.004864513262194309,
"acc_norm": 0.7934674367655845,
"acc_norm_stderr": 0.004039897423689437
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.41,
"acc_stderr": 0.049431107042371025,
"acc_norm": 0.41,
"acc_norm_stderr": 0.049431107042371025
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.674074074074074,
"acc_stderr": 0.040491220417025055,
"acc_norm": 0.674074074074074,
"acc_norm_stderr": 0.040491220417025055
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.8289473684210527,
"acc_stderr": 0.030643607071677084,
"acc_norm": 0.8289473684210527,
"acc_norm_stderr": 0.030643607071677084
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.8,
"acc_stderr": 0.04020151261036843,
"acc_norm": 0.8,
"acc_norm_stderr": 0.04020151261036843
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7735849056603774,
"acc_stderr": 0.025757559893106737,
"acc_norm": 0.7735849056603774,
"acc_norm_stderr": 0.025757559893106737
},
"harness|hendrycksTest-college_biology|5": {
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}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## Dataset Structure
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Who are the source data producers?
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#### Annotation process
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#### Personal and Sensitive Information
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_TeeZee__2xbagel-dpo-34b-v0.2 | [
"region:us"
] | 2024-01-13T23:18:13+00:00 | {"pretty_name": "Evaluation run of TeeZee/2xbagel-dpo-34b-v0.2", "dataset_summary": "Dataset automatically created during the evaluation run of model [TeeZee/2xbagel-dpo-34b-v0.2](https://huggingface.co/TeeZee/2xbagel-dpo-34b-v0.2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_TeeZee__2xbagel-dpo-34b-v0.2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T23:15:59.619735](https://huggingface.co/datasets/open-llm-leaderboard/details_TeeZee__2xbagel-dpo-34b-v0.2/blob/main/results_2024-01-13T23-15-59.619735.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.7214725397684685,\n \"acc_stderr\": 0.029456464928054458,\n \"acc_norm\": 0.7359963920471002,\n \"acc_norm_stderr\": 0.030168902390549673,\n \"mc1\": 0.5018359853121175,\n \"mc1_stderr\": 0.017503383046877048,\n \"mc2\": 0.6715187545754473,\n \"mc2_stderr\": 0.015523811623029661\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6356655290102389,\n \"acc_stderr\": 0.014063260279882417,\n \"acc_norm\": 0.6527303754266212,\n \"acc_norm_stderr\": 0.013913034529620458\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6113324039036049,\n \"acc_stderr\": 0.004864513262194309,\n \"acc_norm\": 0.7934674367655845,\n \"acc_norm_stderr\": 0.004039897423689437\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.674074074074074,\n \"acc_stderr\": 0.040491220417025055,\n \"acc_norm\": 0.674074074074074,\n \"acc_norm_stderr\": 0.040491220417025055\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.8289473684210527,\n \"acc_stderr\": 0.030643607071677084,\n \"acc_norm\": 0.8289473684210527,\n \"acc_norm_stderr\": 0.030643607071677084\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.8,\n \"acc_stderr\": 0.04020151261036843,\n \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.04020151261036843\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.7735849056603774,\n \"acc_stderr\": 0.025757559893106737,\n \"acc_norm\": 0.7735849056603774,\n \"acc_norm_stderr\": 0.025757559893106737\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8819444444444444,\n \"acc_stderr\": 0.02698334650330939,\n \"acc_norm\": 0.8819444444444444,\n \"acc_norm_stderr\": 0.02698334650330939\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 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"latest", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T23-15-59.619735.parquet"]}]}, {"config_name": "harness_hendrycksTest_machine_learning_5", "data_files": [{"split": "2024_01_13T23_15_59.619735", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T23-15-59.619735.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T23-15-59.619735.parquet"]}]}, {"config_name": "harness_hendrycksTest_management_5", "data_files": [{"split": "2024_01_13T23_15_59.619735", "path": ["**/details_harness|hendrycksTest-management|5_2024-01-13T23-15-59.619735.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-management|5_2024-01-13T23-15-59.619735.parquet"]}]}, {"config_name": "harness_hendrycksTest_marketing_5", "data_files": [{"split": "2024_01_13T23_15_59.619735", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-13T23-15-59.619735.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-13T23-15-59.619735.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2024_01_13T23_15_59.619735", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T23-15-59.619735.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T23-15-59.619735.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2024_01_13T23_15_59.619735", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T23-15-59.619735.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T23-15-59.619735.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2024_01_13T23_15_59.619735", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T23-15-59.619735.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T23-15-59.619735.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2024_01_13T23_15_59.619735", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T23-15-59.619735.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T23-15-59.619735.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2024_01_13T23_15_59.619735", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T23-15-59.619735.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T23-15-59.619735.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2024_01_13T23_15_59.619735", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T23-15-59.619735.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T23-15-59.619735.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2024_01_13T23_15_59.619735", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T23-15-59.619735.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T23-15-59.619735.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2024_01_13T23_15_59.619735", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T23-15-59.619735.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T23-15-59.619735.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2024_01_13T23_15_59.619735", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T23-15-59.619735.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T23-15-59.619735.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_01_13T23_15_59.619735", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T23-15-59.619735.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T23-15-59.619735.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_01_13T23_15_59.619735", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T23-15-59.619735.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T23-15-59.619735.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_01_13T23_15_59.619735", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T23-15-59.619735.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T23-15-59.619735.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_01_13T23_15_59.619735", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T23-15-59.619735.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T23-15-59.619735.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_01_13T23_15_59.619735", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T23-15-59.619735.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T23-15-59.619735.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_01_13T23_15_59.619735", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T23-15-59.619735.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T23-15-59.619735.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_13T23_15_59.619735", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T23-15-59.619735.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T23-15-59.619735.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_13T23_15_59.619735", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T23-15-59.619735.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T23-15-59.619735.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_13T23_15_59.619735", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T23-15-59.619735.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T23-15-59.619735.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T23_15_59.619735", "path": ["**/details_harness|winogrande|5_2024-01-13T23-15-59.619735.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T23-15-59.619735.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T23_15_59.619735", "path": ["results_2024-01-13T23-15-59.619735.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T23-15-59.619735.parquet"]}]}]} | 2024-01-13T23:18:35+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of TeeZee/2xbagel-dpo-34b-v0.2
Dataset automatically created during the evaluation run of model TeeZee/2xbagel-dpo-34b-v0.2 on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-13T23:15:59.619735(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of TeeZee/2xbagel-dpo-34b-v0.2\n\n\n\nDataset automatically created during the evaluation run of model TeeZee/2xbagel-dpo-34b-v0.2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T23:15:59.619735(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of TeeZee/2xbagel-dpo-34b-v0.2\n\n\n\nDataset automatically created during the evaluation run of model TeeZee/2xbagel-dpo-34b-v0.2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T23:15:59.619735(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
7d71fbfd61ce56f319afaa0efe74d243a21da081 |
# Dataset Card for Evaluation run of beowolx/MistralHermes-CodePro-7B-v1
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [beowolx/MistralHermes-CodePro-7B-v1](https://huggingface.co/beowolx/MistralHermes-CodePro-7B-v1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_beowolx__MistralHermes-CodePro-7B-v1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T23:16:31.615360](https://huggingface.co/datasets/open-llm-leaderboard/details_beowolx__MistralHermes-CodePro-7B-v1/blob/main/results_2024-01-13T23-16-31.615360.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6355378468432605,
"acc_stderr": 0.03226341558486178,
"acc_norm": 0.6374815210840533,
"acc_norm_stderr": 0.03291019935178123,
"mc1": 0.3488372093023256,
"mc1_stderr": 0.016684419859986893,
"mc2": 0.4966549787597113,
"mc2_stderr": 0.015039415129128687
},
"harness|arc:challenge|25": {
"acc": 0.590443686006826,
"acc_stderr": 0.014370358632472435,
"acc_norm": 0.6245733788395904,
"acc_norm_stderr": 0.01415063143511173
},
"harness|hellaswag|10": {
"acc": 0.629555865365465,
"acc_stderr": 0.004819367172685959,
"acc_norm": 0.8268273252340171,
"acc_norm_stderr": 0.0037762314890081123
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.35,
"acc_stderr": 0.04793724854411022,
"acc_norm": 0.35,
"acc_norm_stderr": 0.04793724854411022
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5851851851851851,
"acc_stderr": 0.04256193767901408,
"acc_norm": 0.5851851851851851,
"acc_norm_stderr": 0.04256193767901408
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6907894736842105,
"acc_stderr": 0.037610708698674805,
"acc_norm": 0.6907894736842105,
"acc_norm_stderr": 0.037610708698674805
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.57,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.57,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6641509433962264,
"acc_stderr": 0.02906722014664483,
"acc_norm": 0.6641509433962264,
"acc_norm_stderr": 0.02906722014664483
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7638888888888888,
"acc_stderr": 0.03551446610810826,
"acc_norm": 0.7638888888888888,
"acc_norm_stderr": 0.03551446610810826
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.45,
"acc_stderr": 0.05,
"acc_norm": 0.45,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.33,
"acc_stderr": 0.047258156262526045,
"acc_norm": 0.33,
"acc_norm_stderr": 0.047258156262526045
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6069364161849711,
"acc_stderr": 0.0372424959581773,
"acc_norm": 0.6069364161849711,
"acc_norm_stderr": 0.0372424959581773
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.37254901960784315,
"acc_stderr": 0.04810840148082636,
"acc_norm": 0.37254901960784315,
"acc_norm_stderr": 0.04810840148082636
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5617021276595745,
"acc_stderr": 0.03243618636108101,
"acc_norm": 0.5617021276595745,
"acc_norm_stderr": 0.03243618636108101
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4824561403508772,
"acc_stderr": 0.04700708033551038,
"acc_norm": 0.4824561403508772,
"acc_norm_stderr": 0.04700708033551038
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5172413793103449,
"acc_stderr": 0.04164188720169375,
"acc_norm": 0.5172413793103449,
"acc_norm_stderr": 0.04164188720169375
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4365079365079365,
"acc_stderr": 0.0255428468174005,
"acc_norm": 0.4365079365079365,
"acc_norm_stderr": 0.0255428468174005
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.49206349206349204,
"acc_stderr": 0.044715725362943486,
"acc_norm": 0.49206349206349204,
"acc_norm_stderr": 0.044715725362943486
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.33,
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"harness|hendrycksTest-us_foreign_policy|5": {
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"harness|gsm8k|5": {
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"acc_stderr": 0.013442502402794302
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## Dataset Structure
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Who are the source data producers?
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#### Annotation process
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#### Personal and Sensitive Information
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_beowolx__MistralHermes-CodePro-7B-v1 | [
"region:us"
] | 2024-01-13T23:18:50+00:00 | {"pretty_name": "Evaluation run of beowolx/MistralHermes-CodePro-7B-v1", "dataset_summary": "Dataset automatically created during the evaluation run of model [beowolx/MistralHermes-CodePro-7B-v1](https://huggingface.co/beowolx/MistralHermes-CodePro-7B-v1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_beowolx__MistralHermes-CodePro-7B-v1\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T23:16:31.615360](https://huggingface.co/datasets/open-llm-leaderboard/details_beowolx__MistralHermes-CodePro-7B-v1/blob/main/results_2024-01-13T23-16-31.615360.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6355378468432605,\n \"acc_stderr\": 0.03226341558486178,\n \"acc_norm\": 0.6374815210840533,\n \"acc_norm_stderr\": 0.03291019935178123,\n \"mc1\": 0.3488372093023256,\n \"mc1_stderr\": 0.016684419859986893,\n \"mc2\": 0.4966549787597113,\n \"mc2_stderr\": 0.015039415129128687\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.590443686006826,\n \"acc_stderr\": 0.014370358632472435,\n \"acc_norm\": 0.6245733788395904,\n \"acc_norm_stderr\": 0.01415063143511173\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.629555865365465,\n \"acc_stderr\": 0.004819367172685959,\n \"acc_norm\": 0.8268273252340171,\n \"acc_norm_stderr\": 0.0037762314890081123\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.04793724854411022,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.04793724854411022\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5851851851851851,\n \"acc_stderr\": 0.04256193767901408,\n \"acc_norm\": 0.5851851851851851,\n \"acc_norm_stderr\": 0.04256193767901408\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6907894736842105,\n \"acc_stderr\": 0.037610708698674805,\n \"acc_norm\": 0.6907894736842105,\n \"acc_norm_stderr\": 0.037610708698674805\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.57,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6641509433962264,\n \"acc_stderr\": 0.02906722014664483,\n \"acc_norm\": 0.6641509433962264,\n \"acc_norm_stderr\": 0.02906722014664483\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7638888888888888,\n \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.7638888888888888,\n \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6069364161849711,\n \"acc_stderr\": 0.0372424959581773,\n \"acc_norm\": 0.6069364161849711,\n \"acc_norm_stderr\": 0.0372424959581773\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.04810840148082636,\n \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.04810840148082636\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5617021276595745,\n \"acc_stderr\": 0.03243618636108101,\n \"acc_norm\": 0.5617021276595745,\n \"acc_norm_stderr\": 0.03243618636108101\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4824561403508772,\n \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.04164188720169375,\n \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.04164188720169375\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.4365079365079365,\n \"acc_stderr\": 0.0255428468174005,\n \"acc_norm\": 0.4365079365079365,\n \"acc_norm_stderr\": 0.0255428468174005\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.49206349206349204,\n \"acc_stderr\": 0.044715725362943486,\n \"acc_norm\": 0.49206349206349204,\n \"acc_norm_stderr\": 0.044715725362943486\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7967741935483871,\n \"acc_stderr\": 0.02289168798455495,\n \"acc_norm\": 0.7967741935483871,\n \"acc_norm_stderr\": 0.02289168798455495\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5073891625615764,\n \"acc_stderr\": 0.035176035403610105,\n \"acc_norm\": 0.5073891625615764,\n \"acc_norm_stderr\": 0.035176035403610105\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.65,\n \"acc_stderr\": 0.04793724854411019,\n \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.04793724854411019\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.793939393939394,\n \"acc_stderr\": 0.03158415324047711,\n 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["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T23-16-31.615360.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2024_01_13T23_16_31.615360", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T23-16-31.615360.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T23-16-31.615360.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2024_01_13T23_16_31.615360", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T23-16-31.615360.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T23-16-31.615360.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2024_01_13T23_16_31.615360", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T23-16-31.615360.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T23-16-31.615360.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_01_13T23_16_31.615360", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T23-16-31.615360.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T23-16-31.615360.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_01_13T23_16_31.615360", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T23-16-31.615360.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T23-16-31.615360.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_01_13T23_16_31.615360", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T23-16-31.615360.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T23-16-31.615360.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_01_13T23_16_31.615360", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T23-16-31.615360.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T23-16-31.615360.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_01_13T23_16_31.615360", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T23-16-31.615360.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T23-16-31.615360.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_01_13T23_16_31.615360", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T23-16-31.615360.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T23-16-31.615360.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_13T23_16_31.615360", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T23-16-31.615360.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T23-16-31.615360.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_13T23_16_31.615360", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T23-16-31.615360.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T23-16-31.615360.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_13T23_16_31.615360", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T23-16-31.615360.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T23-16-31.615360.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T23_16_31.615360", "path": ["**/details_harness|winogrande|5_2024-01-13T23-16-31.615360.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T23-16-31.615360.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T23_16_31.615360", "path": ["results_2024-01-13T23-16-31.615360.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T23-16-31.615360.parquet"]}]}]} | 2024-01-13T23:19:11+00:00 | [] | [] | TAGS
#region-us
|
# Dataset Card for Evaluation run of beowolx/MistralHermes-CodePro-7B-v1
Dataset automatically created during the evaluation run of model beowolx/MistralHermes-CodePro-7B-v1 on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2024-01-13T23:16:31.615360(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of beowolx/MistralHermes-CodePro-7B-v1\n\n\n\nDataset automatically created during the evaluation run of model beowolx/MistralHermes-CodePro-7B-v1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T23:16:31.615360(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of beowolx/MistralHermes-CodePro-7B-v1\n\n\n\nDataset automatically created during the evaluation run of model beowolx/MistralHermes-CodePro-7B-v1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2024-01-13T23:16:31.615360(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
aa2fd412464674ac13464131425d5162ed102b5c | # FAVA Datasets
FAVA datasets include: annotation data and training data.
## Dataset Details
### Annotation Data
The annotation dataset includes 460 annotated passages identifying and editing errors using our hallucination taxonomy. This dataset was used for the fine-grained error detection task, using the annotated passages as the gold passages.
### Training Data
The training data includes 35k training instances of erroneous input and corrected output pairs using our synthetic data generation pipeline.
| fava-uw/fava-data | [
"region:us"
] | 2024-01-13T23:19:52+00:00 | {} | 2024-01-15T04:55:30+00:00 | [] | [] | TAGS
#region-us
| # FAVA Datasets
FAVA datasets include: annotation data and training data.
## Dataset Details
### Annotation Data
The annotation dataset includes 460 annotated passages identifying and editing errors using our hallucination taxonomy. This dataset was used for the fine-grained error detection task, using the annotated passages as the gold passages.
### Training Data
The training data includes 35k training instances of erroneous input and corrected output pairs using our synthetic data generation pipeline.
| [
"# FAVA Datasets\n\nFAVA datasets include: annotation data and training data.",
"## Dataset Details",
"### Annotation Data\n\nThe annotation dataset includes 460 annotated passages identifying and editing errors using our hallucination taxonomy. This dataset was used for the fine-grained error detection task, using the annotated passages as the gold passages.",
"### Training Data\n\nThe training data includes 35k training instances of erroneous input and corrected output pairs using our synthetic data generation pipeline."
] | [
"TAGS\n#region-us \n",
"# FAVA Datasets\n\nFAVA datasets include: annotation data and training data.",
"## Dataset Details",
"### Annotation Data\n\nThe annotation dataset includes 460 annotated passages identifying and editing errors using our hallucination taxonomy. This dataset was used for the fine-grained error detection task, using the annotated passages as the gold passages.",
"### Training Data\n\nThe training data includes 35k training instances of erroneous input and corrected output pairs using our synthetic data generation pipeline."
] |
6f793015a4dd51d931419336b7eee482d3e60e30 |
# Dataset of t91/T91/T91 (Girls' Frontline)
This is the dataset of t91/T91/T91 (Girls' Frontline), containing 12 images and their tags.
The core tags of this character are `blue_hair, hairband, ahoge, short_hair, breasts, bangs, orange_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:--------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 12 | 12.19 MiB | [Download](https://huggingface.co/datasets/CyberHarem/t91_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 12 | 7.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/t91_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 29 | 15.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/t91_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 12 | 11.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/t91_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 29 | 20.77 MiB | [Download](https://huggingface.co/datasets/CyberHarem/t91_girlsfrontline/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/t91_girlsfrontline',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------|
| 0 | 12 |  |  |  |  |  | 1girl, solo, looking_at_viewer, white_background, simple_background, blush, cleavage, gloves, smile |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | white_background | simple_background | blush | cleavage | gloves | smile |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:-------------------|:--------------------|:--------|:-----------|:---------|:--------|
| 0 | 12 |  |  |  |  |  | X | X | X | X | X | X | X | X | X |
| CyberHarem/t91_girlsfrontline | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2024-01-13T23:21:12+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-13T23:23:44+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of t91/T91/T91 (Girls' Frontline)
=========================================
This is the dataset of t91/T91/T91 (Girls' Frontline), containing 12 images and their tags.
The core tags of this character are 'blue\_hair, hairband, ahoge, short\_hair, breasts, bangs, orange\_eyes', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |