Sapbert UMLS 2020AB All Lang From XLMR

🚀 SapBERT-XLMR

SapBERT-XLMR is a cross - lingual biomedical lexical - semantics model trained on UMLS data, which can be used for various biomedical NLP tasks.

✨ Features

• Multilingual Support: Suitable for cross - lingual biomedical tasks.
• Trained on UMLS: Utilizes the UMLS 2020AB dataset for training.
• Based on xlm - roberta - base: Employs the xlm - roberta - base model as the foundation.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

The following script converts a list of strings (entity names) into embeddings.

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)

📚 Documentation

• Model Details: SapBERT (Liu et al. 2020) is trained with UMLS 2020AB, using [xlm - roberta - base](https://huggingface.co/xlm - roberta - base) as the base model. Please use [CLS] as the representation of the input.
• More Details: For more details about training and eval, see SapBERT github repo.

📄 License

No license information is provided in the original document.

📄 Citation

@inproceedings{liu2021learning,
	title={Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking},
	author={Liu, Fangyu and Vuli{\'c}, Ivan and Korhonen, Anna and Collier, Nigel},
	booktitle={Proceedings of ACL-IJCNLP 2021},
	month = aug,
	year={2021}
}

📋 Information Table

📢 News

• A cross - lingual extension of SapBERT will appear in the main onference of ACL 2021!
• SapBERT will appear in the conference proceedings of NAACL 2021!