task_categories:text2text-generation size_categories:10K<n<100K language:en license:odc-by instructions generate instructions instruct region:us
---
license: odc-by
task_categories:
- text2text-generation
language:
- en
tags:
- instructions
- generate instructions
- instruct
size_categories:
- 10K<n<100K
---

# 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)