readme: ---
  license: apache-2.0
  language:
  - en
  tags:
  - biomedical
  - lexical semantics
  - bionlp
  - biology
  - science
  - embedding
  - entity linking
  ---
  ---
  
  
  datasets:
  - UMLS
  
  **[news]** A cross-lingual extension of SapBERT will appear in the main onference of **ACL 2021**! <br>
  **[news]** SapBERT will appear in the conference proceedings of **NAACL 2021**!
  
  ### 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.
  
  ### SapBERT-PubMedBERT
  SapBERT by [Liu et al. (2020)](https://arxiv.org/pdf/2010.11784.pdf). Trained with [UMLS](https://www.nlm.nih.gov/research/umls/licensedcontent/umlsknowledgesources.html) 2020AA (English only), using [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) as the base model.
  
  ### Citation
  ```bibtex
  @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.",
  }
  ```