
# fleece2instructions-inputs-alpaca-cleaned


This data was downloaded from the [alpaca-lora](https://github.com/tloen/alpaca-lora) repo under the `ODC-BY` license (see [snapshot here](https://web.archive.org/web/20230325034703/https://github.com/tloen/alpaca-lora/blob/main/DATA_LICENSE)) and processed to text2text format. The license under which the data was downloaded from the source applies to this repo.

Note that the `inputs` and `instruction` columns in the original dataset have been aggregated together for text2text generation. Each has a token with either `<instruction>` or `<inputs>` in front of the relevant text, both for model understanding and regex separation later.

## Processing details 

- Drop rows with `output` having less then 4 words (via `nltk.word_tokenize`)
- This dataset **does** include both the original `instruction`s and the `inputs` columns, aggregated together into `instructions_inputs`
- In the `instructions_inputs` column, the text is delineated via tokens that are either `<instruction>` or `<inputs>` in front of the relevant text, both for model understanding and regex separation later.

## contents 

```python
DatasetDict({
    train: Dataset({
        features: ['instructions_inputs', 'output'],
        num_rows: 43537
    })
    test: Dataset({
        features: ['instructions_inputs', 'output'],
        num_rows: 2418
    })
    validation: Dataset({
        features: ['instructions_inputs', 'output'],
        num_rows: 2420
    })
})
```

## examples 

![examples](https://i.imgur.com/07PnnPJ.png)

## token counts 


t5

![t5](https://i.imgur.com/FbARV2S.png)




bart


![bart](https://i.imgur.com/tGqtBwA.png)
task_categories:text2text-generation size_categories:10K<n<100K language:en license:odc-by instructions generate instructions instruct region:us
{"language": ["en"], "license": "odc-by", "size_categories": ["10K<n<100K"], "task_categories": ["text2text-generation"], "tags": ["instructions", "generate instructions", "instruct"]}
TAGS
#task_categories-text2text-generation #size_categories-10K<n<100K #language-English #license-odc-by #instructions #generate instructions #instruct #region-us 


# fleece2instructions-inputs-alpaca-cleaned


This data was downloaded from the alpaca-lora repo under the 'ODC-BY' license (see snapshot here) and processed to text2text format. The license under which the data was downloaded from the source applies to this repo.

Note that the 'inputs' and 'instruction' columns in the original dataset have been aggregated together for text2text generation. Each has a token with either '<instruction>' or '<inputs>' in front of the relevant text, both for model understanding and regex separation later.

## Processing details 

- Drop rows with 'output' having less then 4 words (via 'nltk.word_tokenize')
- This dataset does include both the original 'instruction's and the 'inputs' columns, aggregated together into 'instructions_inputs'
- In the 'instructions_inputs' column, the text is delineated via tokens that are either '<instruction>' or '<inputs>' in front of the relevant text, both for model understanding and regex separation later.

## contents 



## examples 

!examples

## token counts 


t5

!t5




bart


!bart
# fleece2instructions-inputs-alpaca-cleaned


This data was downloaded from the alpaca-lora repo under the 'ODC-BY' license (see snapshot here) and processed to text2text format. The license under which the data was downloaded from the source applies to this repo.

Note that the 'inputs' and 'instruction' columns in the original dataset have been aggregated together for text2text generation. Each has a token with either '<instruction>' or '<inputs>' in front of the relevant text, both for model understanding and regex separation later. ## Processing details 

- Drop rows with 'output' having less then 4 words (via 'nltk.word_tokenize')
- This dataset does include both the original 'instruction's and the 'inputs' columns, aggregated together into 'instructions_inputs'
- In the 'instructions_inputs' column, the text is delineated via tokens that are either '<instruction>' or '<inputs>' in front of the relevant text, both for model understanding and regex separation later. ## contents ## examples 

!examples ## token counts 


t5

!t5




bart


!bart
TAGS
#task_categories-text2text-generation #size_categories-10K<n<100K #language-English #license-odc-by #instructions #generate instructions #instruct #region-us 
 # fleece2instructions-inputs-alpaca-cleaned


This data was downloaded from the alpaca-lora repo under the 'ODC-BY' license (see snapshot here) and processed to text2text format. The license under which the data was downloaded from the source applies to this repo.

Note that the 'inputs' and 'instruction' columns in the original dataset have been aggregated together for text2text generation. Each has a token with either '<instruction>' or '<inputs>' in front of the relevant text, both for model understanding and regex separation later. ## Processing details 

- Drop rows with 'output' having less then 4 words (via 'nltk.word_tokenize')
- This dataset does include both the original 'instruction's and the 'inputs' columns, aggregated together into 'instructions_inputs'
- In the 'instructions_inputs' column, the text is delineated via tokens that are either '<instruction>' or '<inputs>' in front of the relevant text, both for model understanding and regex separation later. ## contents ## examples 

!examples ## token counts 


t5

!t5




bart


!bart
passage: TAGS
#task_categories-text2text-generation #size_categories-10K<n<100K #language-English #license-odc-by #instructions #generate instructions #instruct #region-us 
# fleece2instructions-inputs-alpaca-cleaned


This data was downloaded from the alpaca-lora repo under the 'ODC-BY' license (see snapshot here) and processed to text2text format. The license under which the data was downloaded from the source applies to this repo.

Note that the 'inputs' and 'instruction' columns in the original dataset have been aggregated together for text2text generation. Each has a token with either '<instruction>' or '<inputs>' in front of the relevant text, both for model understanding and regex separation later.## Processing details 

- Drop rows with 'output' having less then 4 words (via 'nltk.word_tokenize')
- This dataset does include both the original 'instruction's and the 'inputs' columns, aggregated together into 'instructions_inputs'
- In the 'instructions_inputs' column, the text is delineated via tokens that are either '<instruction>' or '<inputs>' in front of the relevant text, both for model understanding and regex separation later.## contents## examples 

!examples## token counts 


t5

!t5




bart


!bart