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--- |
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language: |
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- en |
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size_categories: |
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- 10K<n<100K |
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task_categories: |
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- conversational |
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pretty_name: Doctor & Patient |
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dataset_info: |
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features: |
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- name: prompt |
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dtype: string |
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- name: input_ids |
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sequence: int32 |
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- name: length |
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dtype: int64 |
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- name: attention_mask |
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sequence: int8 |
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splits: |
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- name: train |
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num_bytes: 42127351.778204426 |
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num_examples: 13125 |
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- name: test |
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num_bytes: 10534245.221795576 |
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num_examples: 3282 |
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download_size: 10917910 |
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dataset_size: 52661597.0 |
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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: test |
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path: data/test-* |
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tags: |
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- biology |
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- medical |
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--- |
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### Dataset |
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This is an edited and tokenized version of the MedQuad-MedicalQnADataset dataset by keivalya. |
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The original dataset contains 16K+ questions and answers between patient and doctor, which have been converted into a full prompt to train BioGPT by Microsoft. |
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##### Tokenizer used |
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microsoft/BioGPT-Large (BPE tokenizer) |
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### Full prompt |
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```py |
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prompt = f"""You are a helpful AI Doctor who answers medical questions. Below is a question from a patient. Your task is to answer the questions as truthfully as you can. |
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### Patient: |
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{sample['Question']} |
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### Doctor: |
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{sample['Answer']}""" |
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``` |
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### Notes |
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Since bioGPT has a max input of 1024, the full prompt was truncated to stay below this limit. |
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The truncation strategy I used made sure that only full sentences were produced. |
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Please note that this dataset is for research/testing only, it should not be used in a real setting or used to give medical advice to people. |
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