| | --- |
| | 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.", |
| | } |
| | ``` |
