--- base_model: - BAAI/bge-small-en-v1.5 datasets: - MedicalQARetrieval - NFCorpus - PublicHealthQA - TRECCOVID - ArguAna language: en license: apache-2.0 metrics: - nDCG - MAP - Recall - Precision - MRR model-index: - name: abhinand/MedEmbed-small-v0.1 results: - dataset: config: en-ext name: MTEB AmazonCounterfactualClassification (en-ext) revision: e8379541af4e31359cca9fbcf4b00f2671dba205 split: test type: mteb/amazon_counterfactual metrics: - type: accuracy value: 72.17391304347827 - type: ap value: 21.757637881353535 - type: ap_weighted value: 21.757637881353535 - type: f1 value: 59.80304692298741 - type: f1_weighted value: 77.3761270422597 - type: main_score value: 72.17391304347827 task: type: Classification - dataset: config: en name: MTEB AmazonCounterfactualClassification (en) revision: e8379541af4e31359cca9fbcf4b00f2671dba205 split: test type: mteb/amazon_counterfactual metrics: - type: accuracy value: 71.28358208955224 - type: ap value: 33.51413347752456 - type: ap_weighted value: 33.51413347752456 - 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type: ndcg_at_1 value: 22.448999999999998 - type: ndcg_at_10 value: 22.198999999999998 - type: ndcg_at_100 value: 34.79 - type: ndcg_at_1000 value: 45.921 - type: ndcg_at_20 value: 23.751 - type: ndcg_at_3 value: 25.185000000000002 - type: ndcg_at_5 value: 24.751 - type: precision_at_1 value: 26.531 - type: precision_at_10 value: 19.592000000000002 - type: precision_at_100 value: 7.327 - type: precision_at_1000 value: 1.486 - type: precision_at_20 value: 15.612 - type: precision_at_3 value: 28.571 - type: precision_at_5 value: 26.939 - type: recall_at_1 value: 1.932 - type: recall_at_10 value: 14.896999999999998 - type: recall_at_100 value: 46.132 - type: recall_at_1000 value: 80.26100000000001 - type: recall_at_20 value: 22.304 - type: recall_at_3 value: 6.237 - type: recall_at_5 value: 9.945 task: type: Retrieval tags: - medembed - medical-embedding - clinical-embedding - information-retrieval - sentence-transformers - mteb --- # MedEmbed: Specialized Embedding Model for Medical and Clinical Information Retrieval ![benchmark-scores](https://cdn-uploads.huggingface.co/production/uploads/60c8619d95d852a24572b025/gTx5-m68LQ3eyNd6fLki2.png) ## Model Description MedEmbed is a family of embedding models fine-tuned specifically for medical and clinical data, designed to enhance performance in healthcare-related natural language processing (NLP) tasks, particularly information retrieval. **GitHub Repo:** [https://github.com/abhinand5/MedEmbed](https://github.com/abhinand5/MedEmbed) **Technical Blog Post:** [https://huggingface.co/blog/abhinand/medembed-finetuned-embedding-models-for-medical-ir](https://huggingface.co/blog/abhinand/medembed-finetuned-embedding-models-for-medical-ir) ## Intended Use This model is intended for use in medical and clinical contexts to improve information retrieval, question answering, and semantic search tasks. It can be integrated into healthcare systems, research tools, and medical literature databases to enhance search capabilities and information access. ## Training Data ![synthetic-datagen-flow](https://cdn-uploads.huggingface.co/production/uploads/60c8619d95d852a24572b025/asaA5QDO_j0PWFQV9NXCu.png) The model was trained using a simple yet effective synthetic data generation pipeline: 1. Source: Clinical notes from PubMed Central (PMC) 2. Processing: [LLaMA 3.1 70B](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct) model used to generate query-response pairs 3. Augmentation: Negative sampling for challenging examples 4. Format: Triplets (query, positive response, negative response) for contrastive learning ## Performance MedEmbed consistently outperforms general-purpose embedding models across various medical NLP benchmarks: - ArguAna - MedicalQARetrieval - NFCorpus - PublicHealthQA - TRECCOVID Specific performance metrics (nDCG, MAP, Recall, Precision, MRR) are available in the full documentation. ## Limitations While highly effective for medical and clinical data, this model may not generalize well to non-medical domains. It should be used with caution in general-purpose NLP tasks. ## Ethical Considerations Users should be aware of potential biases in medical data and the ethical implications of AI in healthcare. This model should be used as a tool to assist, not replace, human expertise in medical decision-making. ## Citation If you use this model in your research, please cite: ```bibtex @software{balachandran2024medembed, author = {Balachandran, Abhinand}, title = {MedEmbed: Medical-Focused Embedding Models}, year = {2024}, url = {https://github.com/abhinand5/MedEmbed} } ``` For more detailed information, visit our GitHub repository.