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--- |
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license: apache-2.0 |
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task_categories: |
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- text-generation |
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language: |
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- zh |
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tags: |
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- medical |
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size_categories: |
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- 1M<n<10M |
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--- |
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# MedicalQA-1.4M |
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MedicalQA is an integrated large-scale, high-quality Chinese SFT dataset, designed for medical knowledge injection into LLMs by SFT or RAG. |
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Each sample is reviewed by the free LLM (ERNIE-Speed) using our proposed Quality Evaluation Algorithm. |
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MedArk-KI involving in two types of medical knowledge: Traditional Chinese Medicine (TCM) and Western Medicine(WM). It consists of 4 subsets as shown in the tabel: |
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| Name | Volume | Author | Reviewer | Type | Source | Source Format | |
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|------|---------|-----------|-------------|------|-------------------------|-----------------| |
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| DX | 5,3554 | Doctor | Doctor | WM | Web | HTML | |
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| HT | 135,8093| ChatGPT | ERNIE-Speed | WM | Books, Encyclopedia, Web| PDF, TXT, HTML | |
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| TCM | 3,8294 | ERNIE 4.0 | ERNIE-Speed | TCM | Books, KG | PDF, Latex, JSON| |
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| MB | 1,4543 |ERNIE-Speed|ERNIE-Speed | WM | Books | Latex | |
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|Totol | 146,4484| | | | | | |
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# 1.DX |
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A total of 5,3554 high-quality Q&A pairs were collected from the DingXiang website, 丁香医生 (https://dxy.com/diseases), and underwent rigorous data cleaning. |
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Each answer was authored by a doctor and reviewed by another doctor, ensuring that all information is accurate and reliable. |
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These Q&A pairs encompass 8 key areas of medical knowledge across 3412 diseases in 31 departments: disease introduction, symptoms, causes, diagnosis, treatment, lifestyle, prevention, and consultation guidance. |
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# 2.HT |
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https://huggingface.co/datasets/FreedomIntelligence/HuatuoGPT2-Pretraining-Instruction |
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Medical_Web_Corpus_cn |
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Medical_Encyclopedia_cn |
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Medical_Books_cn |
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# 3.TCM |
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https://www.dayi.org.cn/ |
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# 4.MB |
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https://github.com/scienceasdf/medical-books |
