MedCPT is a first-of-its-kind Contrastive Pre-trained Transformer model trained with an unprecedented scale of PubMed search logs for zero-shot biomedical information retrieval. MedCPT consists of:
- A frist-stage dense retriever (MedCPT retriever)
- Contains a query encoder (QEnc) and an article encoder (DEnc), both initialized by PubMedBERT.
- Trained by 255M query-article pairs from PubMed search logs and in-batch negatives.
- A second-stage re-ranker (MedCPT re-ranker)
- A transformer cross-encoder (CrossEnc) initialized by PubMedBERT.
- Trained by 18M semantic query-article pairs and localized negatives from the pre-trained MedCPT retriever.
This directory contains:
- Code for training the MedCPT retriever.
- Code for training the MedCPT re-ranker.
- Code for evaluating the pre-trained model.
MedCPT model weights are publicly available on Hugging Face:
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(**encoded).last_hidden_state[:, 0, :]
print(embeds)
print(embeds.size())The output will be:
tensor([[ 0.0413, 0.0084, -0.0491, ..., -0.4963, -0.3830, -0.3593],
[ 0.0801, 0.1193, -0.0905, ..., -0.5380, -0.5059, -0.2944],
[-0.3412, 0.1521, -0.0946, ..., 0.0952, 0.1660, -0.0902]])
torch.Size([3, 768])These embeddings are also in the same space as those generated by 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). [...]",
],
[
"Adipsic diabetes insipidus",
"Adipsic diabetes insipidus (ADI) is a rare but devastating disorder of water balance with significant associated morbidity and mortality. Most patients develop the disease as a result of hypothalamic destruction from a variety of underlying etiologies. [...]",
],
[
"Nephrogenic diabetes insipidus: a comprehensive overview",
"Nephrogenic diabetes insipidus (NDI) is characterized by the inability to concentrate urine that results in polyuria and polydipsia, despite having normal or elevated plasma concentrations of arginine vasopressin (AVP). [...]",
],
]
with torch.no_grad():
# tokenize the queries
encoded = tokenizer(
articles,
truncation=True,
padding=True,
return_tensors='pt',
max_length=512,
)
# encode the queries (use the [CLS] last hidden states as the representations)
embeds = model(**encoded).last_hidden_state[:, 0, :]
print(embeds)
print(embeds.size())The output will be:
tensor([[-0.0189, 0.0115, 0.0988, ..., -0.0655, 0.3155, -0.0357],
[-0.3402, -0.3064, -0.0749, ..., -0.0799, 0.3332, 0.1263],
[-0.2764, -0.0506, -0.0608, ..., 0.0389, 0.2532, 0.1580]])
torch.Size([3, 768])These embeddings are also in the same space as those generated by the MedCPT query encoder.
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("ncbi/MedCPT-Cross-Encoder")
model = AutoModelForSequenceClassification.from_pretrained("ncbi/MedCPT-Cross-Encoder")
query = "diabetes treatment"
# 6 articles to be ranked for the input query
articles = [
"Type 1 and 2 diabetes mellitus: A review on current treatment approach and gene therapy as potential intervention. Type 1 and type 2 diabetes mellitus is a serious and lifelong condition commonly characterised by abnormally elevated blood glucose levels due to a failure in insulin production or a decrease in insulin sensitivity and function. [...]",
"Diabetes mellitus and its chronic complications. Diabetes mellitus is a major cause of morbidity and mortality, and it is a major risk factor for early onset of coronary heart disease. Complications of diabetes are retinopathy, nephropathy, and peripheral neuropathy. [...]",
"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). [...]",
"Adipsic diabetes insipidus. Adipsic diabetes insipidus (ADI) is a rare but devastating disorder of water balance with significant associated morbidity and mortality. Most patients develop the disease as a result of hypothalamic destruction from a variety of underlying etiologies. [...]",
"Nephrogenic diabetes insipidus: a comprehensive overview. Nephrogenic diabetes insipidus (NDI) is characterized by the inability to concentrate urine that results in polyuria and polydipsia, despite having normal or elevated plasma concentrations of arginine vasopressin (AVP). [...]",
"Impact of Salt Intake on the Pathogenesis and Treatment of Hypertension. Excessive dietary salt (sodium chloride) intake is associated with an increased risk for hypertension, which in turn is especially a major risk factor for stroke and other cardiovascular pathologies, but also kidney diseases. Besides, high salt intake or preference for salty food is discussed to be positive associated with stomach cancer, and according to recent studies probably also obesity risk. [...]"
]
# combine query article into pairs
pairs = [[query, article] for article in articles]
with torch.no_grad():
encoded = tokenizer(
pairs,
truncation=True,
padding=True,
return_tensors="pt",
max_length=512,
)
logits = model(**encoded).logits.squeeze(dim=1)
print(logits)The output will be
tensor([ 6.9363, -8.2063, -8.7692, -12.3450, -10.4416, -15.8475])Higher scores indicate higher relevance.
Please first download the MedCPT embeddings of PubMed articles here: https://ftp.ncbi.nlm.nih.gov/pub/lu/MedCPT/pubmed_embeddings/.
For example, let's download the latest 1M articles. Please run:
wget https://ftp.ncbi.nlm.nih.gov/pub/lu/MedCPT/pubmed_embeddings/embeds_chunk_36.npy # these are the embeddings
wget https://ftp.ncbi.nlm.nih.gov/pub/lu/MedCPT/pubmed_embeddings/pmids_chunk_36.json # these are the coresponding PMIDs
wget https://ftp.ncbi.nlm.nih.gov/pub/lu/MedCPT/pubmed_embeddings/pubmed_chunk_36.json # these are the PMID contentThen run the following example code:
import faiss
import torch
import numpy as np
import json
from transformers import AutoTokenizer, AutoModel
# building the Faiss index of PubMed articles, let's use the flat inner product index
pubmed_embeds = np.load("embeds_chunk_36.npy")
index = faiss.IndexFlatIP(768)
index.add(pubmed_embeds)
# these are the corresponding pmids for the article embeddings
pmids = json.load(open("pmids_chunk_36.json"))
model = AutoModel.from_pretrained("ncbi/MedCPT-Query-Encoder")
tokenizer = AutoTokenizer.from_pretrained("ncbi/MedCPT-Query-Encoder")
queries = [
"How to treat diabetes with COVID-19?",
"Are mRNA vaccines safe for children?"
]
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(**encoded).last_hidden_state[:, 0, :]
# search the Faiss index
scores, inds = index.search(embeds, k=10)
# print the search results
for idx, query in enumerate(queries):
print(f"Query: {query}")
for score, ind in zip(scores[idx], inds[idx]):
print(f"PMID: {pmids[ind]}; Score: {score}")The output will be:
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
Query: How to treat diabetes with COVID-19?
PMID: 36890686; Score: 67.43043518066406
PMID: 36882971; Score: 67.43043518066406
PMID: 36201275; Score: 67.2848892211914
PMID: 36369292; Score: 67.06027221679688
PMID: 36089790; Score: 66.66815948486328
PMID: 36028964; Score: 66.57908630371094
PMID: 36100987; Score: 66.34353637695312
PMID: 36188145; Score: 66.2936019897461
PMID: 36579192; Score: 66.26624298095703
PMID: 36060943; Score: 66.16267395019531
Query: Are mRNA vaccines safe for children?
PMID: 36621604; Score: 70.50843811035156
PMID: 36974643; Score: 70.31710815429688
PMID: 36160352; Score: 69.89913940429688
PMID: 36048728; Score: 69.77267456054688
PMID: 36404633; Score: 68.93994903564453
PMID: 36634021; Score: 68.82601928710938
PMID: 36694479; Score: 68.5921859741211
PMID: 36881800; Score: 68.4502182006836
PMID: 36689319; Score: 68.39824676513672
PMID: 36405931; Score: 68.20221710205078Due to privacy concerns, we are not able to release the PubMed user logs. As a surrogate, we provide the question-article pair data from BioASQ in this repo as example training datasets. You can convert your data to the example data formats and train the MedCPT model.
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.
If you find this repo helpful, please cite MedCPT by:
@article{jin2023medcpt,
title={MedCPT: Contrastive Pre-trained Transformers with large-scale PubMed search logs for zero-shot biomedical information retrieval},
author={Jin, Qiao and Kim, Won and Chen, Qingyu and Comeau, Donald C and Yeganova, Lana and Wilbur, W John and Lu, Zhiyong},
journal={Bioinformatics},
volume={39},
number={11},
pages={btad651},
year={2023},
publisher={Oxford University Press}
}