task_categories:text2text-generation task_categories:text-generation size_categories:10K<n<100K language:en license:cc-by-nc-4.0 instructions domain adaptation region:us
---
license: cc-by-nc-4.0
task_categories:
- text2text-generation
- text-generation
language:
- en
size_categories:
- 10K<n<100K
tags:
- instructions
- domain adaptation
---
# codealpaca for text2text generation 


This dataset was downloaded from the [sahil280114/codealpaca](https://github.com/sahil280114/codealpaca) github repo and parsed into text2text format for "generating" instructions.

It was downloaded under the **wonderful** Creative Commons Attribution-NonCommercial 4.0 International Public License (see snapshots of the [repo](https://web.archive.org/web/20230325040745/https://github.com/sahil280114/codealpaca) and [data license](https://web.archive.org/web/20230325041314/https://github.com/sahil280114/codealpaca/blob/master/DATA_LICENSE)), so that license applies to this dataset.

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.

## structure

dataset structure:

```python
DatasetDict({
    train: Dataset({
        features: ['instructions_inputs', 'output'],
        num_rows: 18014
    })
    test: Dataset({
        features: ['instructions_inputs', 'output'],
        num_rows: 1000
    })
    validation: Dataset({
        features: ['instructions_inputs', 'output'],
        num_rows: 1002
    })
})
```
## example 

The example shows what rows **without** inputs will look like (approximately 60% of the dataset according to repo). Note the special tokens to identify what is what when the model generates text: `<instruction>` and `<input>`:


![example](https://i.imgur.com/bdZM4NW.png)

## token lengths

bart

![bart](https://i.imgur.com/81qBl3e.png)

t5


![t5](https://i.imgur.com/63vOqP4.png)