README.md

metadata

id: sap_umls_MedRoBERTa.nl_meantoken
name: sap_umls_MedRoBERTa.nl_meantoken
description: >-
  MedRoBERTa.nl continued pre-training on hard medical terms pairs from the UMLS
  ontology, using the multi-similarity loss function
license: gpl-3.0
language: nl
tags:
  - bionlp
  - lexical semantic
  - biology
  - embedding
  - biomedical
  - science
  - entity linking
pipeline_tag: feature-extraction

Model Card for Sap Umls Medroberta.Nl Meantoken

The model was trained on medical entity triplets (anchor, term, synonym)

Expected input and output

The input should be a string of biomedical entity names, e.g., "covid infection" or "Hydroxychloroquine". The [CLS] embedding of the last layer is regarded as the output.

Extracting embeddings from sap_umls_MedRoBERTa.nl_meantoken

The following script converts a list of strings (entity names) into embeddings.

import numpy as np
import torch
from tqdm.auto import tqdm
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("UMCU/sap_umls_MedRoBERTa.nl_meantoken")
model = AutoModel.from_pretrained("UMCU/sap_umls_MedRoBERTa.nl_meantoken").cuda()

# replace with your own list of entity names
all_names = ["covid-19", "Coronavirus infection", "high fever", "Tumor of posterior wall of oropharynx"]

bs = 128 # batch size during inference
all_embs = []
for i in tqdm(np.arange(0, len(all_names), bs)):
    toks = tokenizer.batch_encode_plus(all_names[i:i+bs],
                                       padding="max_length",
                                       max_length=25,
                                       truncation=True,
                                       return_tensors="pt")
    toks_cuda = {}
    for k,v in toks.items():
        toks_cuda[k] = v.cuda()
    cls_rep = model(**toks_cuda)[0].mean(1) 
    all_embs.append(cls_rep.cpu().detach().numpy())

all_embs = np.concatenate(all_embs, axis=0)

Data description

Hard Dutch UMLS synonym pairs (terms referring to the same CUI). Dutch UMLS extended with matching Dutch SNOMEDCT term, and including English medication names

Acknowledgement

This is part of the DT4H project.

Doi and reference

For more details about training and eval, see SapBERT github repo.

Citation

@inproceedings{liu-etal-2021-self,
    title = "Self-Alignment Pretraining for Biomedical Entity Representations",
    author = "Liu, Fangyu  and
      Shareghi, Ehsan  and
      Meng, Zaiqiao  and
      Basaldella, Marco  and
      Collier, Nigel",
    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2021.naacl-main.334",
    pages = "4228--4238",
    abstract = "Despite the widespread success of self-supervised learning via masked language models (MLM), accurately capturing fine-grained semantic relationships in the biomedical domain remains a challenge. This is of paramount importance for entity-level tasks such as entity linking where the ability to model entity relations (especially synonymy) is pivotal. To address this challenge, we propose SapBERT, a pretraining scheme that self-aligns the representation space of biomedical entities. We design a scalable metric learning framework that can leverage UMLS, a massive collection of biomedical ontologies with 4M+ concepts. In contrast with previous pipeline-based hybrid systems, SapBERT offers an elegant one-model-for-all solution to the problem of medical entity linking (MEL), achieving a new state-of-the-art (SOTA) on six MEL benchmarking datasets. In the scientific domain, we achieve SOTA even without task-specific supervision. With substantial improvement over various domain-specific pretrained MLMs such as BioBERT, SciBERTand and PubMedBERT, our pretraining scheme proves to be both effective and robust.",
}