| --- |
| language: |
| - en |
| size_categories: |
| - 10K<n<100K |
| task_categories: |
| - conversational |
| pretty_name: Doctor & Patient |
| dataset_info: |
| features: |
| - name: prompt |
| dtype: string |
| - name: input_ids |
| sequence: int32 |
| - name: length |
| dtype: int64 |
| - name: attention_mask |
| sequence: int8 |
| splits: |
| - name: train |
| num_bytes: 42127351.778204426 |
| num_examples: 13125 |
| - name: test |
| num_bytes: 10534245.221795576 |
| num_examples: 3282 |
| download_size: 10917910 |
| dataset_size: 52661597.0 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| - split: test |
| path: data/test-* |
| tags: |
| - biology |
| - medical |
| --- |
| |
| ### Dataset |
| This is an edited and tokenized version of the MedQuad-MedicalQnADataset dataset by keivalya. |
| 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. |
|
|
| ##### Tokenizer used |
| microsoft/BioGPT-Large (BPE tokenizer) |
|
|
|
|
| ### Full prompt |
|
|
| ```py |
| 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. |
| |
| ### Patient: |
| {sample['Question']} |
| |
| ### Doctor: |
| {sample['Answer']}""" |
| ``` |
|
|
| ### Notes |
| Since bioGPT has a max input of 1024, the full prompt was truncated to stay below this limit. |
| The truncation strategy I used made sure that only full sentences were produced. |
|
|
| 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. |
|
|
|
|
