MedCPT Article Encoder EU AI Act Compliance Profile

MedCPT Introduction

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).

This repo contains the MedCPT Article Encoder.

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.

Case 1. Using the MedCPT Article Encoder

import torch
from transformers import AutoTokenizer, AutoModel

model = AutoModel.from_pretrained("ncbi/MedCPT-Article-Encoder")
tokenizer = AutoTokenizer.from_pretrained("ncbi/MedCPT-Article-Encoder")

# each article contains a list of two texts (usually a title and an abstract)
articles = [
	[
		"Diagnosis and Management of Central Diabetes Insipidus in Adults",
		"Central diabetes insipidus (CDI) is a clinical syndrome which results from loss or impaired function of vasopressinergic neurons in the hypothalamus/posterior pituitary, resulting in impaired synthesis and/or secretion of arginine vasopressin (AVP). [

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