Datasets:

bagga005
/

medredqa

dataset_info:
  features:
    - name: id
      dtype: string
    - name: title
      dtype: string
    - name: body
      dtype: string
    - name: response
      dtype: string
    - name: response_score
      dtype: float32
    - name: occupation
      dtype: string
    - name: pmcids
      dtype: string
  splits:
    - name: train
      num_bytes: 59383722
      num_examples: 40792
    - name: validation
      num_bytes: 7307243
      num_examples: 5100
    - name: test
      num_bytes: 7343299
      num_examples: 5099
  download_size: 46290063
  dataset_size: 74034264
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*

MedRedQA

HuggingFace upload of a QA dataset of questions and responses from qualified medical professionals sourced from MedRedQA. If used, please cite the original authors using the citation below.

Dataset Details

Dataset Description

The dataset contains three splits:

train: 40792 questions and responses from a qualified expert
test: 5099 questions and responses from a qualified expert
validation: 5100 questions and responses from a qualified expert

Dataset Sources

Repository: https://data.csiro.au/collection/csiro:62454
Paper: https://aclanthology.org/2023.ijcnlp-main.42/

Direct Use

import json
from datasets import load_dataset, Dataset

if __name__ == "__main__":
    # load only train and validation splits
    medredqa_train, medredqa_eval = load_dataset("bagga005/medredqa", split=["train", "validation"])
    print("# of train:", len(medredqa_train), "# of validation:", len(medredqa_eval))
    
    print("\nmedredqa_train\n", json.dumps(medredqa_train[0], indent=2))
    print("\nmedredqa_eval\n", json.dumps(medredqa_eval[0], indent=2))

License

As per MedRedQA, this dataset is licensed under Creative Commons Attribution Noncommercial-Share Alike 4.0 Licence Below paper must be cited when using this dataset

Citation

@inproceedings{nguyen-etal-2023-medredqa,
    title = "{M}ed{R}ed{QA} for Medical Consumer Question Answering: Dataset, Tasks, and Neural Baselines",
    author = "Nguyen, Vincent  and
      Karimi, Sarvnaz  and
      Rybinski, Maciej  and
      Xing, Zhenchang",
    editor = "Park, Jong C.  and
      Arase, Yuki  and
      Hu, Baotian  and
      Lu, Wei  and
      Wijaya, Derry  and
      Purwarianti, Ayu  and
      Krisnadhi, Adila Alfa",
    booktitle = "Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = nov,
    year = "2023",
    address = "Nusa Dua, Bali",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.ijcnlp-main.42/",
    doi = "10.18653/v1/2023.ijcnlp-main.42",
    pages = "629--648"
}