MedCPT Query 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 Query 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 Query Encoder

import torch
from transformers import AutoTokenizer, AutoModel

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

queries = [
	"diabetes treatment", 
	"How to treat diabetes?", 
	"A 45-year-old man presents with increased thirst and frequent urination over the past 3 months.",
]

with torch.no_grad():
	# tokenize the queries
	encoded = tokenizer(
		queries, 
		truncation=True, 
		padding=True, 
		return_tensors='pt', 
		max_length=64,
	)
	
	# encode the queries (use the [CLS] last hidden states as the representations)
	embeds = model(

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