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--- |
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dataset_info: |
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features: |
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- name: id |
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dtype: string |
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- name: subreddit |
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dtype: string |
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- name: title |
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dtype: string |
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- name: post |
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dtype: string |
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- name: summary |
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dtype: string |
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- name: query_token |
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sequence: int64 |
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- name: query |
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dtype: string |
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- name: reference_response |
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dtype: string |
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- name: reference_response_token |
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sequence: int64 |
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- name: reference_response_token_len |
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dtype: int64 |
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- name: query_reference_response |
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dtype: string |
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- name: query_reference_response_token |
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sequence: int64 |
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- name: query_reference_response_token_len |
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dtype: int64 |
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splits: |
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- name: train |
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num_bytes: 1600440249 |
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num_examples: 116722 |
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- name: validation |
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num_bytes: 88425771 |
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num_examples: 6447 |
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- name: test |
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num_bytes: 89922466 |
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num_examples: 6553 |
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download_size: 551824607 |
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dataset_size: 1778788486 |
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--- |
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# TL;DR SFT Dataset for OpenAI's [Summarize from Feedback](https://openai.com/blog/summarization/) task |
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The dataset is directly taken from https://github.com/openai/summarize-from-feedback/tree/700967448d10004279f138666442bf1497d0e705#reddit-tldr-dataset |
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These columns are taken directly from the aforementioned dataset: |
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* **id**: unique identifier for the post |
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* **subreddit**: subreddit the post was taken from |
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* **title**: title of the post |
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* **post**: body of the post |
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* **summary**: summary of the post |
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* **reference_response**: reference response for the post |
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These columns are added by this preprocessing script: |
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* **query**: length-limited query for summarization: OAI pre-processes the main text (title + subreddit + post), ensuring it has only 512 tokens; if the main text is too long, then it tries to truncate at the last ` |
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`. If it's too short it pads the main text ([summarize_from_feedback/tasks.py#L98-L165](https://github.com/openai/summarize-from-feedback/blob/700967448d10004279f138666442bf1497d0e705/summarize_from_feedback/tasks.py#L98-L165)). Padding is either space or `[PAD]` token (see Args below). |
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* **query_token**: tokenized version of `query` |
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* **reference_response_token**: tokenized version of `reference_response` |
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* **reference_response_token_len**: length of `reference_response_token` |
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* **query_reference_response**: concatenation of `query.strip()` and `reference_response` |
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* **query_reference_response_token**: tokenized version of `query_reference_response`, up to `max_sft_query_response_length` tokens |
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* **query_reference_response_token_len**: length of `query_reference_response_token` |
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# Args |
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```python |
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{'base_model': 'EleutherAI/pythia-1b-deduped', |
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'check_length_correctness': False, |
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'cnndm_params': TaskQueryHParams(length=1919, |
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format_str='Article:\n{article}\n\nTL;DR:\n', |
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truncate_field='article', |
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truncate_text='\n', |
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padding=[50277], |
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pad_side='left', |
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max_sft_response_length=None, |
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max_sft_query_response_length=None, |
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max_rm_response_length=155, |
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max_rm_query_response_length=2021), |
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'hf_entity': 'cleanrl', |
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'push_to_hub': True, |
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'tldr_params': TaskQueryHParams(length=512, |
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format_str='SUBREDDIT: r/{subreddit}\n' |
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'\n' |
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'TITLE: {title}\n' |
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'\n' |
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'POST: {post}\n' |
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'\n' |
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'TL;DR:', |
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truncate_field='post', |
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truncate_text='\n', |
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padding=[50277], |
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pad_side='left', |
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max_sft_response_length=53, |
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max_sft_query_response_length=562, |
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max_rm_response_length=169, |
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max_rm_query_response_length=638)} |
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``` |
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