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README.md
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### Expected input and output
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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.
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### SapBERT-PubMedBERT
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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.
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### Citation
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```bibtex
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@inproceedings{liu-etal-2021-self,
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### Expected input and output
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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.
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#### Extracting embeddings from SapBERT
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The following script converts a list of strings (entity names) into embeddings.
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```python
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import numpy as np
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import torch
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from tqdm.auto import tqdm
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from transformers import AutoTokenizer, AutoModel
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tokenizer = AutoTokenizer.from_pretrained("cambridgeltl/SapBERT-from-PubMedBERT-fulltext")
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model = AutoModel.from_pretrained("cambridgeltl/SapBERT-from-PubMedBERT-fulltext").cuda()
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# replace with your own list of entity names
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all_names = ["covid-19", "Coronavirus infection", "high fever", "Tumor of posterior wall of oropharynx"]
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bs = 128 # batch size during inference
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all_embs = []
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for i in tqdm(np.arange(0, len(all_names), bs)):
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toks = tokenizer.batch_encode_plus(all_names[i:i+bs],
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padding="max_length",
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max_length=25,
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truncation=True,
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return_tensors="pt")
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toks_cuda = {}
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for k,v in toks.items():
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toks_cuda[k] = v.cuda()
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cls_rep = model(**toks_cuda)[0][:,0,:] # use CLS representation as the embedding
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all_embs.append(cls_rep.cpu().detach().numpy())
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all_embs = np.concatenate(all_embs, axis=0)
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```
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For more details about training and eval, see SapBERT [github repo](https://github.com/cambridgeltl/sapbert).
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### SapBERT-PubMedBERT
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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.
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### Citation
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```bibtex
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@inproceedings{liu-etal-2021-self,
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