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--- |
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license: cc |
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language: |
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- en |
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library_name: transformers |
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pipeline_tag: text-generation |
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tags: |
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- medical |
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--- |
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# MedAlpaca 13b |
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## Table of Contents |
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[Model Description](#model-description) |
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- [Architecture](#architecture) |
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- [Training Data](#trainig-data) |
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[Model Usage](#model-usage) |
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[Limitations](#limitations) |
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## Model Description |
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### Architecture |
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`medalpaca-13b` is a large language model specifically fine-tuned for medical domain tasks. |
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It is based on LLaMA (Large Language Model Meta AI) and contains 13 billion parameters. |
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The primary goal of this model is to improve question-answering and medical dialogue tasks. |
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### Training Data |
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The training data for this project was sourced from various resources. |
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Firstly, we used Anki flashcards to automatically generate questions, |
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from the front of the cards and anwers from the back of the card. |
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Secondly, we generated medical question-answer pairs from [Wikidoc](https://www.wikidoc.org/index.php/Main_Page). |
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We extracted paragraphs with relevant headings, and used Chat-GPT 3.5 |
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to generate questions from the headings and using the corresponding paragraphs |
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as answers. This dataset is still under development and we believe |
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that approximately 70% of these question answer pairs are factual correct. |
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Thirdly, we used StackExchange to extract question-answer pairs, taking the |
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top-rated question from five categories: Academia, Bioinformatics, Biology, |
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Fitness, and Health. Additionally, we used a dataset from [ChatDoctor](https://arxiv.org/abs/2303.14070) |
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consisting of 200,000 question-answer pairs, available at https://github.com/Kent0n-Li/ChatDoctor. |
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| Source | n items | |
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|------------------------------|--------| |
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| ChatDoc large | 200000 | |
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| wikidoc | 67704 | |
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| Stackexchange academia | 40865 | |
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| Anki flashcards | 33955 | |
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| Stackexchange biology | 27887 | |
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| Stackexchange fitness | 9833 | |
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| Stackexchange health | 7721 | |
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| Wikidoc patient information | 5942 | |
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| Stackexchange bioinformatics | 5407 | |
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## Model Usage |
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To evaluate the performance of the model on a specific dataset, you can use the Hugging Face Transformers library's built-in evaluation scripts. Please refer to the evaluation guide for more information. |
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Inference |
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You can use the model for inference tasks like question-answering and medical dialogues using the Hugging Face Transformers library. Here's an example of how to use the model for a question-answering task: |
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```python |
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from transformers import pipeline |
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qa_pipeline = pipeline("question-answering", model="medalpaca/medalpaca-13b", tokenizer="medalpaca/medalpaca-13b") |
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question = "What are the symptoms of diabetes?" |
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context = "Diabetes is a metabolic disease that causes high blood sugar. The symptoms include increased thirst, frequent urination, and unexplained weight loss." |
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answer = qa_pipeline({"question": question, "context": context}) |
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print(answer) |
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``` |
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## Limitations |
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The model may not perform effectively outside the scope of the medical domain. |
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The training data primarily targets the knowledge level of medical students, |
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which may result in limitations when addressing the needs of board-certified physicians. |
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The model has not been tested in real-world applications, so its efficacy and accuracy are currently unknown. |
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It should never be used as a substitute for a doctor's opinion and must be treated as a research tool only. |
