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README.md

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+---
+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.",
+}
+```

config.json

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+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "type_vocab_size": 2,
+  "vocab_size": 30522
+}

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gitattributes

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special_tokens_map.json

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tokenizer_config.json

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