Update README.md

README.md

@@ -15,6 +15,41 @@ datasets:
 ### SapBERT-XLMR
 SapBERT [(Liu et al. 2021)](https://arxiv.org/pdf/2010.11784.pdf) trained with [UMLS](https://www.nlm.nih.gov/research/umls/licensedcontent/umlsknowledgesources.html) 2020AB, using [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) as the base model. Please use [CLS] as the representation of the input.
 
+
+#### 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