
# Fork of [SirNeural/flan_v2](https://huggingface.co/datasets/SirNeural/flan_v2)

just in case it gets deleted. 

# Dataset Card for Flan V2

## Dataset Description

- **Homepage:** https://ai.googleblog.com/2023/02/the-flan-collection-advancing-open.html
- **Repository:** https://github.com/google-research/FLAN/tree/main/flan/v2
- **Paper:** https://arxiv.org/abs/2301.13688
- **Leaderboard:** 
- **Point of Contact:** 

### Dataset Summary

This is a processed version of the Flan V2 dataset.

I'm not affiliated with the creators, I'm just releasing the files in an easier-to-access format after processing.

The authors of the Flan Collection recommend experimenting with different mixing ratio's of tasks to get optimal results downstream.

This current version I've processed is missing a few datasets compared to the main branch of the flan v2 repo:
- cs-en WMT translation task requires manual download and I wasn't able to get the credentials
- q_re_cc dataset preprocessing for the dialog task wasn't working
- 
These are minor hits to the total size of the collection (orders of MB compared to GB) but once those are fixed I will upload a complete version.

## Dataset Structure

### Data Instances

Flan 2021 (flan), P3 (t0), Super-Natural Instructions (niv2), Chain-of-thought (cot), and Dialog (dialog)

### Data Fields

Instruction data comes in a few formats:
- Few Shot (fs)
- Zero Shot (zs)
- Options Provided in context (i.e. multiple choice pick one) (opt)
- No Options Provided (noopt)

Each combination of the above tasks + formats are saved as a JSONL with following schema `{"input": ..., "target": ..., "task": ...}`

### Data Splits

Everything is saved as a train split

license:apache-2.0 flan flan 2022 flan v2 arxiv:2301.13688 region:us
{"license": "apache-2.0", "pretty_name": "Flan v2", "tags": ["flan", "flan 2022", "flan v2"], "duplicated_from": "SirNeural/flan_v2"}
TAGS
#license-apache-2.0 #flan #flan 2022 #flan v2 #arxiv-2301.13688 #region-us 


# Fork of SirNeural/flan_v2

just in case it gets deleted. 

# Dataset Card for Flan V2

## Dataset Description

- Homepage: URL
- Repository: URL
- Paper: URL
- Leaderboard: 
- Point of Contact: 

### Dataset Summary

This is a processed version of the Flan V2 dataset.

I'm not affiliated with the creators, I'm just releasing the files in an easier-to-access format after processing.

The authors of the Flan Collection recommend experimenting with different mixing ratio's of tasks to get optimal results downstream.

This current version I've processed is missing a few datasets compared to the main branch of the flan v2 repo:
- cs-en WMT translation task requires manual download and I wasn't able to get the credentials
- q_re_cc dataset preprocessing for the dialog task wasn't working
- 
These are minor hits to the total size of the collection (orders of MB compared to GB) but once those are fixed I will upload a complete version.

## Dataset Structure

### Data Instances

Flan 2021 (flan), P3 (t0), Super-Natural Instructions (niv2), Chain-of-thought (cot), and Dialog (dialog)

### Data Fields

Instruction data comes in a few formats:
- Few Shot (fs)
- Zero Shot (zs)
- Options Provided in context (i.e. multiple choice pick one) (opt)
- No Options Provided (noopt)

Each combination of the above tasks + formats are saved as a JSONL with following schema '{"input": ..., "target": ..., "task": ...}'

### Data Splits

Everything is saved as a train split

# Fork of SirNeural/flan_v2

just in case it gets deleted. # Dataset Card for Flan V2 ## Dataset Description

- Homepage: URL
- Repository: URL
- Paper: URL
- Leaderboard: 
- Point of Contact: ### Dataset Summary

This is a processed version of the Flan V2 dataset.

I'm not affiliated with the creators, I'm just releasing the files in an easier-to-access format after processing.

The authors of the Flan Collection recommend experimenting with different mixing ratio's of tasks to get optimal results downstream.

This current version I've processed is missing a few datasets compared to the main branch of the flan v2 repo:
- cs-en WMT translation task requires manual download and I wasn't able to get the credentials
- q_re_cc dataset preprocessing for the dialog task wasn't working
- 
These are minor hits to the total size of the collection (orders of MB compared to GB) but once those are fixed I will upload a complete version. ## Dataset Structure ### Data Instances

Flan 2021 (flan), P3 (t0), Super-Natural Instructions (niv2), Chain-of-thought (cot), and Dialog (dialog) ### Data Fields

Instruction data comes in a few formats:
- Few Shot (fs)
- Zero Shot (zs)
- Options Provided in context (i.e. multiple choice pick one) (opt)
- No Options Provided (noopt)

Each combination of the above tasks + formats are saved as a JSONL with following schema '{"input": ..., "target": ..., "task": ...}' ### Data Splits

Everything is saved as a train split
TAGS
#license-apache-2.0 #flan #flan 2022 #flan v2 #arxiv-2301.13688 #region-us 
 # Fork of SirNeural/flan_v2

just in case it gets deleted. # Dataset Card for Flan V2 ## Dataset Description

- Homepage: URL
- Repository: URL
- Paper: URL
- Leaderboard: 
- Point of Contact: ### Dataset Summary

This is a processed version of the Flan V2 dataset.

I'm not affiliated with the creators, I'm just releasing the files in an easier-to-access format after processing.

The authors of the Flan Collection recommend experimenting with different mixing ratio's of tasks to get optimal results downstream.

This current version I've processed is missing a few datasets compared to the main branch of the flan v2 repo:
- cs-en WMT translation task requires manual download and I wasn't able to get the credentials
- q_re_cc dataset preprocessing for the dialog task wasn't working
- 
These are minor hits to the total size of the collection (orders of MB compared to GB) but once those are fixed I will upload a complete version. ## Dataset Structure ### Data Instances

Flan 2021 (flan), P3 (t0), Super-Natural Instructions (niv2), Chain-of-thought (cot), and Dialog (dialog) ### Data Fields

Instruction data comes in a few formats:
- Few Shot (fs)
- Zero Shot (zs)
- Options Provided in context (i.e. multiple choice pick one) (opt)
- No Options Provided (noopt)

Each combination of the above tasks + formats are saved as a JSONL with following schema '{"input": ..., "target": ..., "task": ...}' ### Data Splits

Everything is saved as a train split