# Dataset Card for Flan-mini

## Dataset Description

- **Repository:** https://github.com/declare-lab/flacuna
- **Paper:** https://arxiv.org/abs//2307.02053
- **Leaderboard:** https://declare-lab.net/instruct-eval/
- **Point of Contact:** sporia@sutd.edu.sg

### Dataset Summary

Given the enormous size of the Flan Collection, we opted to work with a carefully selected subset that maintains a high level of task diversity while reducing the overall dataset size. In the Table below, we present the specific tasks included in our subset of Flan, along with their respective dataset sizes. 
As the public release of the Flan Collection does not include programming tasks, we augment the collection with existing code datasets.
Specifically, we include CodeContests, APPS, and CodeSearchNet.
Following the data processing pipeline of Flan Collection, we sample a fixed number of examples from each dataset, where each example is randomly augmented with different prompt templates.
Specifically, the examples are processed with a pool of handcrafted prompt templates and may be used as zero-shot examples or grouped together with few-shot demonstrations.
We incorporated various ChatGPT datasets, including Alpaca, Code Alpaca, and ShareGPT, into our Flan-mini collection.

| Dataset Name                | Source                 | Dataset Size |
|-----------------------------|------------------------|--------------|
| Flan2021                    | Flan                   | 388K         |
| Public Pool of Prompts      | Flan                   | 320K         |
| Natural instructions v2     | Flan                   | 200K         |
| CoT                         | Flan                   | 100K         |
| Code Search                 | HF/code_search_net | 100K         |
| Code Contest                | HF/deepmind/code_contests      | 50K          |
| Apps                        | HF/codeparrot/apps      | 50K          |
| GPT4-Alpaca                 | GPT-4                  | 52K          |
| Code-Alpaca                 | ChatGPT                | 20K          |
| ShareGPT                    | ChatGPT                | 60K          |
| Total                       | -                      | 1.34M        |

## Dataset Structure

### Data Instances

[More Information Needed]

### Data Fields

[More Information Needed]

### Citation Information

```bibtex
@misc{ghosal2023flacuna,
      title={Flacuna: Unleashing the Problem Solving Power of Vicuna using FLAN Fine-Tuning}, 
      author={Deepanway Ghosal and Yew Ken Chia and Navonil Majumder and Soujanya Poria},
      year={2023},
      eprint={2307.02053},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```
size_categories:1M<n<10M license:cc arxiv:2307.02053 region:us
{"license": "cc", "size_categories": ["1M<n<10M"], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "conversations", "dtype": "list"}], "splits": [{"name": "train", "num_examples": 1340153}]}}
TAGS
#size_categories-1M<n<10M #license-cc #arxiv-2307.02053 #region-us 

Dataset Card for Flan-mini
==========================


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


* Repository: URL
* Paper: URL/2307.02053
* Leaderboard: URL
* Point of Contact: sporia@URL


### Dataset Summary


Given the enormous size of the Flan Collection, we opted to work with a carefully selected subset that maintains a high level of task diversity while reducing the overall dataset size. In the Table below, we present the specific tasks included in our subset of Flan, along with their respective dataset sizes.
As the public release of the Flan Collection does not include programming tasks, we augment the collection with existing code datasets.
Specifically, we include CodeContests, APPS, and CodeSearchNet.
Following the data processing pipeline of Flan Collection, we sample a fixed number of examples from each dataset, where each example is randomly augmented with different prompt templates.
Specifically, the examples are processed with a pool of handcrafted prompt templates and may be used as zero-shot examples or grouped together with few-shot demonstrations.
We incorporated various ChatGPT datasets, including Alpaca, Code Alpaca, and ShareGPT, into our Flan-mini collection.


Dataset Name: Flan2021, Source: Flan, Dataset Size: 388K
Dataset Name: Public Pool of Prompts, Source: Flan, Dataset Size: 320K
Dataset Name: Natural instructions v2, Source: Flan, Dataset Size: 200K
Dataset Name: CoT, Source: Flan, Dataset Size: 100K
Dataset Name: Code Search, Source: HF/code\_search\_net, Dataset Size: 100K
Dataset Name: Code Contest, Source: HF/deepmind/code\_contests, Dataset Size: 50K
Dataset Name: Apps, Source: HF/codeparrot/apps, Dataset Size: 50K
Dataset Name: GPT4-Alpaca, Source: GPT-4, Dataset Size: 52K
Dataset Name: Code-Alpaca, Source: ChatGPT, Dataset Size: 20K
Dataset Name: ShareGPT, Source: ChatGPT, Dataset Size: 60K
Dataset Name: Total, Source: -, Dataset Size: 1.34M


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


### Data Instances


### Data Fields



### Dataset Summary


Given the enormous size of the Flan Collection, we opted to work with a carefully selected subset that maintains a high level of task diversity while reducing the overall dataset size. In the Table below, we present the specific tasks included in our subset of Flan, along with their respective dataset sizes.
As the public release of the Flan Collection does not include programming tasks, we augment the collection with existing code datasets.
Specifically, we include CodeContests, APPS, and CodeSearchNet.
Following the data processing pipeline of Flan Collection, we sample a fixed number of examples from each dataset, where each example is randomly augmented with different prompt templates.
Specifically, the examples are processed with a pool of handcrafted prompt templates and may be used as zero-shot examples or grouped together with few-shot demonstrations.
We incorporated various ChatGPT datasets, including Alpaca, Code Alpaca, and ShareGPT, into our Flan-mini collection.


Dataset Name: Flan2021, Source: Flan, Dataset Size: 388K
Dataset Name: Public Pool of Prompts, Source: Flan, Dataset Size: 320K
Dataset Name: Natural instructions v2, Source: Flan, Dataset Size: 200K
Dataset Name: CoT, Source: Flan, Dataset Size: 100K
Dataset Name: Code Search, Source: HF/code\_search\_net, Dataset Size: 100K
Dataset Name: Code Contest, Source: HF/deepmind/code\_contests, Dataset Size: 50K
Dataset Name: Apps, Source: HF/codeparrot/apps, Dataset Size: 50K
Dataset Name: GPT4-Alpaca, Source: GPT-4, Dataset Size: 52K
Dataset Name: Code-Alpaca, Source: ChatGPT, Dataset Size: 20K
Dataset Name: ShareGPT, Source: ChatGPT, Dataset Size: 60K
Dataset Name: Total, Source: -, Dataset Size: 1.34M


Dataset Structure
----------------- ### Data Instances ### Data Fields
TAGS
#size_categories-1M<n<10M #license-cc #arxiv-2307.02053 #region-us 
 ### Dataset Summary


Given the enormous size of the Flan Collection, we opted to work with a carefully selected subset that maintains a high level of task diversity while reducing the overall dataset size. In the Table below, we present the specific tasks included in our subset of Flan, along with their respective dataset sizes.
As the public release of the Flan Collection does not include programming tasks, we augment the collection with existing code datasets.
Specifically, we include CodeContests, APPS, and CodeSearchNet.
Following the data processing pipeline of Flan Collection, we sample a fixed number of examples from each dataset, where each example is randomly augmented with different prompt templates.
Specifically, the examples are processed with a pool of handcrafted prompt templates and may be used as zero-shot examples or grouped together with few-shot demonstrations.
We incorporated various ChatGPT datasets, including Alpaca, Code Alpaca, and ShareGPT, into our Flan-mini collection.


Dataset Name: Flan2021, Source: Flan, Dataset Size: 388K
Dataset Name: Public Pool of Prompts, Source: Flan, Dataset Size: 320K
Dataset Name: Natural instructions v2, Source: Flan, Dataset Size: 200K
Dataset Name: CoT, Source: Flan, Dataset Size: 100K
Dataset Name: Code Search, Source: HF/code\_search\_net, Dataset Size: 100K
Dataset Name: Code Contest, Source: HF/deepmind/code\_contests, Dataset Size: 50K
Dataset Name: Apps, Source: HF/codeparrot/apps, Dataset Size: 50K
Dataset Name: GPT4-Alpaca, Source: GPT-4, Dataset Size: 52K
Dataset Name: Code-Alpaca, Source: ChatGPT, Dataset Size: 20K
Dataset Name: ShareGPT, Source: ChatGPT, Dataset Size: 60K
Dataset Name: Total, Source: -, Dataset Size: 1.34M


Dataset Structure
----------------- ### Data Instances ### Data Fields
passage: TAGS
#size_categories-1M<n<10M #license-cc #arxiv-2307.02053 #region-us 
