| --- |
| 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**! |
|
|
| ### 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. |
|
|
| ### 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 SapBERT |
|
|
| The following script converts a list of strings (entity names) into embeddings. |
| ```python |
| import numpy as np |
| import torch |
| from tqdm.auto import tqdm |
| from transformers import AutoTokenizer, AutoModel |
| |
| tokenizer = AutoTokenizer.from_pretrained("cambridgeltl/SapBERT-from-PubMedBERT-fulltext") |
| model = AutoModel.from_pretrained("cambridgeltl/SapBERT-from-PubMedBERT-fulltext").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][:,0,:] # use CLS representation as the embedding |
| all_embs.append(cls_rep.cpu().detach().numpy()) |
| |
| all_embs = np.concatenate(all_embs, axis=0) |
| ``` |
|
|
| For more details about training and eval, see SapBERT [github repo](https://github.com/cambridgeltl/sapbert). |
|
|
|
|
| ### 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.", |
| } |
| ``` |
