
# Dataset Card for "anli"

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:**
- **Repository:** [https://github.com/facebookresearch/anli/](https://github.com/facebookresearch/anli/)
- **Paper:** [Adversarial NLI: A New Benchmark for Natural Language Understanding](https://arxiv.org/abs/1910.14599)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 18.62 MB
- **Size of the generated dataset:** 77.12 MB
- **Total amount of disk used:** 95.75 MB

### Dataset Summary

The Adversarial Natural Language Inference (ANLI) is a new large-scale NLI benchmark dataset,
The dataset is collected via an iterative, adversarial human-and-model-in-the-loop procedure.
ANLI is much more difficult than its predecessors including SNLI and MNLI.
It contains three rounds. Each round has train/dev/test splits.

### Supported Tasks and Leaderboards

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Languages

English

## Dataset Structure

### Data Instances

#### plain_text

- **Size of downloaded dataset files:** 18.62 MB
- **Size of the generated dataset:** 77.12 MB
- **Total amount of disk used:** 95.75 MB

An example of 'train_r2' looks as follows.
```
This example was too long and was cropped:

{
    "hypothesis": "Idris Sultan was born in the first month of the year preceding 1994.",
    "label": 0,
    "premise": "\"Idris Sultan (born January 1993) is a Tanzanian Actor and comedian, actor and radio host who won the Big Brother Africa-Hotshot...",
    "reason": "",
    "uid": "ed5c37ab-77c5-4dbc-ba75-8fd617b19712"
}
```

### Data Fields

The data fields are the same among all splits.

#### plain_text
- `uid`: a `string` feature.
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
- `reason`: a `string` feature.

### Data Splits

|   name   |train_r1|dev_r1|train_r2|dev_r2|train_r3|dev_r3|test_r1|test_r2|test_r3|
|----------|-------:|-----:|-------:|-----:|-------:|-----:|------:|------:|------:|
|plain_text|   16946|  1000|   45460|  1000|  100459|  1200|   1000|   1000|   1200|

## Dataset Creation

### Curation Rationale

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### Who are the source language producers?

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Annotations

#### Annotation process

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### Who are the annotators?

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Personal and Sensitive Information

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Discussion of Biases

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Other Known Limitations

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## Additional Information

### Dataset Curators

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Licensing Information

[cc-4 Attribution-NonCommercial](https://github.com/facebookresearch/anli/blob/main/LICENSE)

### Citation Information

```
@InProceedings{nie2019adversarial,
    title={Adversarial NLI: A New Benchmark for Natural Language Understanding},
    author={Nie, Yixin
                and Williams, Adina
                and Dinan, Emily
                and Bansal, Mohit
                and Weston, Jason
                and Kiela, Douwe},
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    year = "2020",
    publisher = "Association for Computational Linguistics",
}

```


### Contributions

Thanks to [@thomwolf](https://github.com/thomwolf), [@easonnie](https://github.com/easonnie), [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.
task_categories:text-classification task_ids:natural-language-inference task_ids:multi-input-text-classification annotations_creators:crowdsourced annotations_creators:machine-generated language_creators:found multilinguality:monolingual size_categories:100K<n<1M source_datasets:original source_datasets:extended|hotpot_qa language:en license:cc-by-nc-4.0 arxiv:1910.14599 region:us
{"annotations_creators": ["crowdsourced", "machine-generated"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-nc-4.0"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original", "extended|hotpot_qa"], "task_categories": ["text-classification"], "task_ids": ["natural-language-inference", "multi-input-text-classification"], "paperswithcode_id": "anli", "pretty_name": "Adversarial NLI", "dataset_info": {"config_name": "plain_text", "features": [{"name": "uid", "dtype": "string"}, {"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}, {"name": "reason", "dtype": "string"}], "splits": [{"name": "train_r1", "num_bytes": 8006888, "num_examples": 16946}, {"name": "dev_r1", "num_bytes": 573428, "num_examples": 1000}, {"name": "test_r1", "num_bytes": 574917, "num_examples": 1000}, {"name": "train_r2", "num_bytes": 20801581, "num_examples": 45460}, {"name": "dev_r2", "num_bytes": 556066, "num_examples": 1000}, {"name": "test_r2", "num_bytes": 572639, "num_examples": 1000}, {"name": "train_r3", "num_bytes": 44720719, "num_examples": 100459}, {"name": "dev_r3", "num_bytes": 663148, "num_examples": 1200}, {"name": "test_r3", "num_bytes": 657586, "num_examples": 1200}], "download_size": 26286748, "dataset_size": 77126972}, "configs": [{"config_name": "plain_text", "data_files": [{"split": "train_r1", "path": "plain_text/train_r1-*"}, {"split": "dev_r1", "path": "plain_text/dev_r1-*"}, {"split": "test_r1", "path": "plain_text/test_r1-*"}, {"split": "train_r2", "path": "plain_text/train_r2-*"}, {"split": "dev_r2", "path": "plain_text/dev_r2-*"}, {"split": "test_r2", "path": "plain_text/test_r2-*"}, {"split": "train_r3", "path": "plain_text/train_r3-*"}, {"split": "dev_r3", "path": "plain_text/dev_r3-*"}, {"split": "test_r3", "path": "plain_text/test_r3-*"}], "default": true}]}
TAGS
#task_categories-text-classification #task_ids-natural-language-inference #task_ids-multi-input-text-classification #annotations_creators-crowdsourced #annotations_creators-machine-generated #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #source_datasets-extended|hotpot_qa #language-English #license-cc-by-nc-4.0 #arxiv-1910.14599 #region-us 

Dataset Card for "anli"
=======================


Table of Contents
-----------------


* Dataset Description
	+ Dataset Summary
	+ Supported Tasks and Leaderboards
	+ Languages
* Dataset Structure
	+ Data Instances
	+ Data Fields
	+ Data Splits
* Dataset Creation
	+ Curation Rationale
	+ Source Data
	+ Annotations
	+ Personal and Sensitive Information
* Considerations for Using the Data
	+ Social Impact of Dataset
	+ Discussion of Biases
	+ Other Known Limitations
* Additional Information
	+ Dataset Curators
	+ Licensing Information
	+ Citation Information
	+ Contributions


Dataset Description
-------------------


* Homepage:
* Repository: URL
* Paper: Adversarial NLI: A New Benchmark for Natural Language Understanding
* Point of Contact:
* Size of downloaded dataset files: 18.62 MB
* Size of the generated dataset: 77.12 MB
* Total amount of disk used: 95.75 MB


### Dataset Summary


The Adversarial Natural Language Inference (ANLI) is a new large-scale NLI benchmark dataset,
The dataset is collected via an iterative, adversarial human-and-model-in-the-loop procedure.
ANLI is much more difficult than its predecessors including SNLI and MNLI.
It contains three rounds. Each round has train/dev/test splits.


### Supported Tasks and Leaderboards


### Languages


English


Dataset Structure
-----------------


### Data Instances


#### plain\_text


* Size of downloaded dataset files: 18.62 MB
* Size of the generated dataset: 77.12 MB
* Total amount of disk used: 95.75 MB


An example of 'train\_r2' looks as follows.


### Data Fields


The data fields are the same among all splits.


#### plain\_text


* 'uid': a 'string' feature.
* 'premise': a 'string' feature.
* 'hypothesis': a 'string' feature.
* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2).
* 'reason': a 'string' feature.


### Data Splits



Dataset Creation
----------------


### Curation Rationale


### Source Data


#### Initial Data Collection and Normalization


#### Who are the source language producers?


### Annotations


#### Annotation process


#### Who are the annotators?


### Personal and Sensitive Information


Considerations for Using the Data
---------------------------------


### Social Impact of Dataset


### Discussion of Biases


### Other Known Limitations


Additional Information
----------------------


### Dataset Curators


### Licensing Information


cc-4 Attribution-NonCommercial


### Contributions


Thanks to @thomwolf, @easonnie, @lhoestq, @patrickvonplaten for adding this dataset.



### Dataset Summary


The Adversarial Natural Language Inference (ANLI) is a new large-scale NLI benchmark dataset,
The dataset is collected via an iterative, adversarial human-and-model-in-the-loop procedure.
ANLI is much more difficult than its predecessors including SNLI and MNLI.
It contains three rounds. Each round has train/dev/test splits. ### Supported Tasks and Leaderboards ### Languages


English


Dataset Structure
----------------- ### Data Instances #### plain\_text


* Size of downloaded dataset files: 18.62 MB
* Size of the generated dataset: 77.12 MB
* Total amount of disk used: 95.75 MB


An example of 'train\_r2' looks as follows. ### Data Fields


The data fields are the same among all splits. #### plain\_text


* 'uid': a 'string' feature.
* 'premise': a 'string' feature.
* 'hypothesis': a 'string' feature.
* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2).
* 'reason': a 'string' feature. ### Data Splits



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Licensing Information


cc-4 Attribution-NonCommercial ### Contributions


Thanks to @thomwolf, @easonnie, @lhoestq, @patrickvonplaten for adding this dataset.
TAGS
#task_categories-text-classification #task_ids-natural-language-inference #task_ids-multi-input-text-classification #annotations_creators-crowdsourced #annotations_creators-machine-generated #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #source_datasets-extended|hotpot_qa #language-English #license-cc-by-nc-4.0 #arxiv-1910.14599 #region-us 
 ### Dataset Summary


The Adversarial Natural Language Inference (ANLI) is a new large-scale NLI benchmark dataset,
The dataset is collected via an iterative, adversarial human-and-model-in-the-loop procedure.
ANLI is much more difficult than its predecessors including SNLI and MNLI.
It contains three rounds. Each round has train/dev/test splits. ### Supported Tasks and Leaderboards ### Languages


English


Dataset Structure
----------------- ### Data Instances #### plain\_text


* Size of downloaded dataset files: 18.62 MB
* Size of the generated dataset: 77.12 MB
* Total amount of disk used: 95.75 MB


An example of 'train\_r2' looks as follows. ### Data Fields


The data fields are the same among all splits. #### plain\_text


* 'uid': a 'string' feature.
* 'premise': a 'string' feature.
* 'hypothesis': a 'string' feature.
* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2).
* 'reason': a 'string' feature. ### Data Splits



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Licensing Information


cc-4 Attribution-NonCommercial ### Contributions


Thanks to @thomwolf, @easonnie, @lhoestq, @patrickvonplaten for adding this dataset.
passage: TAGS
#task_categories-text-classification #task_ids-natural-language-inference #task_ids-multi-input-text-classification #annotations_creators-crowdsourced #annotations_creators-machine-generated #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #source_datasets-extended|hotpot_qa #language-English #license-cc-by-nc-4.0 #arxiv-1910.14599 #region-us 
### Dataset Summary


The Adversarial Natural Language Inference (ANLI) is a new large-scale NLI benchmark dataset,
The dataset is collected via an iterative, adversarial human-and-model-in-the-loop procedure.
ANLI is much more difficult than its predecessors including SNLI and MNLI.
It contains three rounds. Each round has train/dev/test splits.### Supported Tasks and Leaderboards### Languages


English


Dataset Structure
-----------------### Data Instances#### plain\_text


* Size of downloaded dataset files: 18.62 MB
* Size of the generated dataset: 77.12 MB
* Total amount of disk used: 95.75 MB


An example of 'train\_r2' looks as follows.### Data Fields


The data fields are the same among all splits.#### plain\_text


* 'uid': a 'string' feature.
* 'premise': a 'string' feature.
* 'hypothesis': a 'string' feature.
* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2).
* 'reason': a 'string' feature.### Data Splits



Dataset Creation
----------------### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?