MedEmbed-large-v0.1/README.md

language, tags, license, datasets, metrics, base_model, model-index

language	tags	license	datasets	metrics	base_model	model-index
en	medembed medical-embedding clinical-embedding information-retrieval sentence-transformers mteb	apache-2.0	MedicalQARetrieval NFCorpus PublicHealthQA TRECCOVID ArguAna	nDCG MAP Recall Precision MRR	BAAI/bge-large-en-v1.5	name results abhinand/MedEmbed-large-v0.1 dataset metrics task config name revision split type default MTEB CMedQAv2-reranking (default) 23d186750531a14a0357ca22cd92d712fd512ea0 test C-MTEB/CMedQAv2-reranking type value map 18.3837 type value mrr 21.1936 type value nAUC_map_max 34.185 type value nAUC_map_std 17.8023 type value nAUC_map_diff1 29.8803 type value nAUC_mrr_max 33.5715 type value nAUC_mrr_std 18.198700000000002 type value nAUC_mrr_diff1 28.1289 type value main_score 18.3837 type Reranking dataset metrics task config name revision split type en MTEB CUREv1 (en) 3bcf51c91e04d04a8a3329dfbe988b964c5cbe83 all clinia/CUREv1 type value ndcg_at_1 60.775 type value ndcg_at_3 55.233 type value ndcg_at_5 53.738 type value ndcg_at_10 52.898999999999994 type value ndcg_at_20 53.64 type value ndcg_at_100 60.18 type value ndcg_at_1000 68.304 type value map_at_1 9.185 type value map_at_3 16.432 type value map_at_5 20.197000000000003 type value map_at_10 25.278 type value map_at_20 29.92 type value map_at_100 38.107 type value map_at_1000 41.919000000000004 type value recall_at_1 9.185 type value recall_at_3 17.996000000000002 type value recall_at_5 23.671 type value recall_at_10 32.817 type value recall_at_20 43.415 type value recall_at_100 70.08 type value recall_at_1000 94.03099999999999 type value precision_at_1 71.95 type value precision_at_3 59.567 type value precision_at_5 53.480000000000004 type value precision_at_10 44.025 type value precision_at_20 35.147 type value precision_at_100 16.997 type value precision_at_1000 3.399 type value mrr_at_1 71.95 type value mrr_at_3 79.4917 type value mrr_at_5 80.5892 type value mrr_at_10 80.9957 type value mrr_at_20 81.1267 type value mrr_at_100 81.164 type value mrr_at_1000 81.1657 type value 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MedEmbed: Specialized Embedding Model for Medical and Clinical Information Retrieval

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

Technical Blog Post: 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

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 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:

@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.