
This dataset contains a selection of Q&A-related tasks gathered and cleaned from the webGPT_comparisons set and the databricks-dolly-15k set. 
Unicode escapes were explicitly removed, and wikipedia citations in the "output" were stripped through regex to hopefully help any
end-product model ignore these artifacts within their input context.

This data is formatted for use in the alpaca instruction format, however the instruction, input, and output columns are kept separate in
the raw data to allow for other configurations. The data has been filtered so that every entry is less than our chosen truncation length of
1024 (LLaMA-style) tokens with the format:
```
"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{instruction}

### Input:
{inputt}

### Response:
{output}"""
```

<h3>webGPT</h3>
This set was filtered from the webGPT_comparisons data by taking any Q&A option that was positively or neutrally-rated by humans (e.g. "score" >= 0).
This might not provide the ideal answer, but this dataset was assembled specifically for extractive Q&A with less regard for how humans
feel about the results.

This selection comprises 23826 of the total entries in the data.

<h3>Dolly</h3>
The dolly data was selected primarily to focus on closed-qa tasks. For this purpose, only entries in the "closed-qa", "information_extraction",
"summarization", "classification", and "creative_writing" were used. While not all of these include a context, they were judged to help
flesh out the training set.

This selection comprises 5362 of the total entries in the data.

task_categories:question-answering size_categories:10K<n<100K license:cc-by-sa-3.0 region:us
{"license": "cc-by-sa-3.0", "size_categories": ["10K<n<100K"], "task_categories": ["question-answering"]}
TAGS
#task_categories-question-answering #size_categories-10K<n<100K #license-cc-by-sa-3.0 #region-us 


This dataset contains a selection of Q&A-related tasks gathered and cleaned from the webGPT_comparisons set and the databricks-dolly-15k set. 
Unicode escapes were explicitly removed, and wikipedia citations in the "output" were stripped through regex to hopefully help any
end-product model ignore these artifacts within their input context.

This data is formatted for use in the alpaca instruction format, however the instruction, input, and output columns are kept separate in
the raw data to allow for other configurations. The data has been filtered so that every entry is less than our chosen truncation length of
1024 (LLaMA-style) tokens with the format:


<h3>webGPT</h3>
This set was filtered from the webGPT_comparisons data by taking any Q&A option that was positively or neutrally-rated by humans (e.g. "score" >= 0).
This might not provide the ideal answer, but this dataset was assembled specifically for extractive Q&A with less regard for how humans
feel about the results.

This selection comprises 23826 of the total entries in the data.

<h3>Dolly</h3>
The dolly data was selected primarily to focus on closed-qa tasks. For this purpose, only entries in the "closed-qa", "information_extraction",
"summarization", "classification", and "creative_writing" were used. While not all of these include a context, they were judged to help
flesh out the training set.

This selection comprises 5362 of the total entries in the data.

TAGS
#task_categories-question-answering #size_categories-10K<n<100K #license-cc-by-sa-3.0 #region-us 

passage: TAGS
#task_categories-question-answering #size_categories-10K<n<100K #license-cc-by-sa-3.0 #region-us 
