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--- |
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license: agpl-3.0 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: val |
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path: data/val-* |
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- split: test |
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path: data/test-* |
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dataset_info: |
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features: |
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- name: input |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 136602851.95652175 |
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num_examples: 7260 |
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- name: val |
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num_bytes: 17065948.584650856 |
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num_examples: 907 |
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- name: test |
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num_bytes: 17084764.40447958 |
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num_examples: 908 |
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download_size: 82888007 |
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dataset_size: 170753564.9456522 |
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task_categories: |
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- text-generation |
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- question-answering |
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- summarization |
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language: |
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- en |
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tags: |
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- biology |
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- biomedicine |
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pretty_name: PubMed Referenced Question Answering Dataset |
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size_categories: |
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- 10M<n<100M |
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--- |
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# Dataset description |
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The PQAref dataset is a dataset for fine-tuning large language models for referenced question-answering in biomedical domain. |
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The dataset contains 3 components: |
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- Instruction - question that is supposed to be answered |
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- Abstracts - set of 10 relevant abstracts retrieved from PubMed by an IR system. They contain the PubMed id, abstract title and the content of the abstract |
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- Answer - expected answer, with references in the form of PubMed IDs. |
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The dataset was created semi-automatically, utilizing questions available from PubMedQA dataset. |
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# arXiv paper |
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https://arxiv.org/abs/2407.05015 |
