Datasets:

Modalities:
Text
Formats:
json
ArXiv:
Libraries:
Datasets
pandas
License:
ApolloCorpus / README.md
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metadata
license: apache-2.0
configs:
  - config_name: pretrain_text
    data_files:
      - split: medicalBook_en
        path: train/pretrain/medicalBook_en_text.json
      - split: medicalBook_zh
        path: train/pretrain/medicalBook_zh_text.json
      - split: medicalGuideline_en
        path: train/pretrain/medicalGuideline_en_text.json
      - split: medicalPaper_en
        path: train/pretrain/medicalPaper_en_text.json
      - split: medicalPaper_es
        path: train/pretrain/medicalPaper_es_text.json
      - split: medicalPaper_fr
        path: train/pretrain/medicalPaper_fr_text.json
      - split: medicalPaper_zh
        path: train/pretrain/medicalPaper_zh_text.json
      - split: medicalWeb_en
        path: train/pretrain/medicalWeb_en_text.json
      - split: medicalWeb_es
        path: train/pretrain/medicalWeb_es_text.json
      - split: medicalWeb_zh
        path: train/pretrain/medicalWeb_zh_text.json
      - split: medicalWiki_en
        path: train/pretrain/medicalWiki_en_text.json
      - split: medicalWiki_fr
        path: train/pretrain/medicalWiki_fr_text.json
      - split: medicalWiki_hi
        path: train/pretrain/medicalWiki_hi_text.json

Multilingual Medicine: Model, Dataset, Benchmark, Code

Covering English, Chinese, French, Hindi, Spanish, Hindi, Arabic So far

👨🏻‍💻Github •📃 Paper • 🌐 Demo • 🤗 ApolloCorpus • 🤗 XMedBench
中文 | English

Apollo

🌈 Update

  • [2024.03.07] Paper released.
  • [2024.02.12] ApolloCorpus and XMedBench is published!🎉
  • [2024.01.23] Apollo repo is published!🎉

Results

Apollo-0.5B • 🤗 Apollo-1.8B • 🤗 Apollo-2B • 🤗 Apollo-6B • 🤗 Apollo-7B

Click to expand

Apollo

Data: Huge, Diverse, Clean, Multilingual

Apollo

Usage

  • Zip File
  • Data category
    • Pretrain:

      • json_name: {data_source}_{language}_{data_type}.json
        • data_type: medicalBook, medicalGuideline, medicalPaper, medicalWeb(from online forum), medicalWiki
        • language: en(English), zh(chinese), es(spanish), fr(french), hi(Hindi)
        • data_type: qa(generated qa from text)
      • data item:
        • data_type==text: list of string
          [
            "string1",
            "string2",
            ...
          ]
          
        • data_type==qa: list of qa pairs(list of string)
          [
            [
              "q1",
              "a1",
              "q2",
              "a2",
              ...
            ],
            ...
          ]
          
    • SFT:

      • json_name: {data_source}_{language}.json
        • data_type: code, general, math, medicalExam, medicalPatient
      • data item: list of qa pairs(list of string)
          [
            [
              "q1",
              "a1",
              "q2",
              "a2",
              ...
            ],
            ...
          ]
        

Citation

@misc{wang2024apollo,
   title={Apollo: Lightweight Multilingual Medical LLMs towards Democratizing Medical AI to 6B People},
   author={Xidong Wang and Nuo Chen and Junyin Chen and Yan Hu and Yidong Wang and Xiangbo Wu and Anningzhe Gao and Xiang Wan and Haizhou Li and Benyou Wang},
   year={2024},
   eprint={2403.03640},
   archivePrefix={arXiv},
   primaryClass={cs.CL}
}