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**MedCPT generates embeddings of biomedical texts that can be used for semantic search (dense retrieval)**. The model contains two encoders: - MedCPT Query Encoder: compute the embeddings of short texts (e.g., questions, search queries, sentences). - MedCPT Article Encoder: compute the embeddings of articles (e.g., PubMed titles & abstracts). **MedCPT has been pre-trained by an unprecedented scale of 255M query-article pairs from PubMed search logs**, and has been shown to achieve state-of-the-art performance on several zero-shot biomedical IR datasets. In general, there are three use cases: 1. Query-to-article search with both encoders. 2. Query representation for clustering or query-to-query search with the query encoder. 3. Article representation for clustering or article-to-article search with the article encoder. For more details, please check out our paper (Bioinformatics, 2023). Please note that the released version is slightly different from the version reported in the paper.

Embeddings & similarity
Input Text→ Output Embedding
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This work was supported by the Intramural Research Programs of the National Institutes of Health, National Library of Medicine. This tool shows the results of research conducted in the Computational Biology Branch, NCBI/NLM. The information produced on this website is not intended for direct diagnostic use or medical decision-making without review and oversight by a clinical professional. Individuals should not change their health behavior solely on the basis of information produced on this website. NIH does not independently verify the validity or utility of the information produced by this tool. If you have questions about the information produced on this website, please see a health care professional. More information about NCBI's disclaimer policy is available.

Text analysis
Input Text→ Output Text
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**MedCPT generates embeddings of biomedical texts that can be used for semantic search (dense retrieval)**. The model contains two encoders: - MedCPT Query Encoder: compute the embeddings of short texts (e.g., questions, search queries, sentences). - MedCPT Article Encoder: compute the embeddings of articles (e.g., PubMed titles & abstracts). **MedCPT has been pre-trained by an unprecedented scale of 255M query-article pairs from PubMed search logs**, and has been shown to achieve state-of-the-art performance on several zero-shot biomedical IR datasets. In general, there are three use cases: 1. Query-to-article search with both encoders. 2. Query representation for clustering or query-to-query search with the query encoder. 3. Article representation for clustering or article-to-article search with the article encoder. For more details, please check out our paper (Bioinformatics, 2023). Please note that the released version is slightly different from the version reported in the paper.

Embeddings & similarity
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗
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