# 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)
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
{"language": ["en"], "license": "cc-by-nc-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["text2text-generation", "text-generation"], "tags": ["instructions", "domain adaptation"]}
TAGS
#task_categories-text2text-generation #task_categories-text-generation #size_categories-10K<n<100K #language-English #license-cc-by-nc-4.0 #instructions #domain adaptation #region-us 

# codealpaca for text2text generation 


This dataset was downloaded from the 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 and 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:


## 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

## token lengths

bart

!bart

t5


!t5
# codealpaca for text2text generation 


This dataset was downloaded from the 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 and 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: ## 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 ## token lengths

bart

!bart

t5


!t5
TAGS
#task_categories-text2text-generation #task_categories-text-generation #size_categories-10K<n<100K #language-English #license-cc-by-nc-4.0 #instructions #domain adaptation #region-us 
 # codealpaca for text2text generation 


This dataset was downloaded from the 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 and 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: ## 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 ## token lengths

bart

!bart

t5


!t5
passage: TAGS
#task_categories-text2text-generation #task_categories-text-generation #size_categories-10K<n<100K #language-English #license-cc-by-nc-4.0 #instructions #domain adaptation #region-us 
# codealpaca for text2text generation 


This dataset was downloaded from the 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 and 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:## 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## token lengths

bart

!bart

t5


!t5