|
--- |
|
id: mirrorbert_MedRoBERTa.nl_clstoken |
|
name: mirrorbert_MedRoBERTa.nl_clstoken |
|
description: MedRoBERTa.nl continued pre-training on hard medical terms pairs from |
|
the SNOMED and UMLS ontology, using the infoNCE loss function |
|
license: gpl-3.0 |
|
language: nl |
|
tags: |
|
- biomedical |
|
- embedding |
|
- lexical semantic |
|
- entity linking |
|
- bionlp |
|
- science |
|
- biology |
|
pipeline_tag: feature-extraction |
|
--- |
|
|
|
# Model Card for Mirrorbert Medroberta.Nl Clstoken |
|
|
|
The model was trained on about 8 millions medical entity pairs (term, synonym) |
|
|
|
|
|
### 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 mirrorbert_MedRoBERTa.nl_clstoken |
|
|
|
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("UMCU/mirrorbert_MedRoBERTa.nl_clstoken") |
|
model = AutoModel.from_pretrained("UMCU/mirrorbert_MedRoBERTa.nl_clstoken").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,:] |
|
all_embs.append(cls_rep.cpu().detach().numpy()) |
|
|
|
all_embs = np.concatenate(all_embs, axis=0) |
|
``` |
|
|
|
|
|
# Data description |
|
|
|
Hard Dutch ontological synonym pairs (terms referring to the same CUI/SCUI). |
|
|
|
|
|
# Acknowledgement |
|
|
|
This is part of the [DT4H project](https://www.datatools4heart.eu/). |
|
|
|
# Doi and reference |
|
|
|
|
|
|
|
For more details about training and eval, see MirrorBERT [github repo](https://github.com/cambridgeltl/mirror-bert). |
|
|
|
|
|
### Citation |
|
```bibtex |
|
@inproceedings{liu-etal-2021-fast, |
|
title = "Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders", |
|
author = "Liu, Fangyu and |
|
Vuli{'c}, Ivan and |
|
Korhonen, Anna and |
|
Collier, Nigel", |
|
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", |
|
month = nov, |
|
year = "2021", |
|
address = "Online and Punta Cana, Dominican Republic", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://aclanthology.org/2021.emnlp-main.109", |
|
pages = "1442--1459", |
|
} |
|
``` |
|
For more details about training/eval and other scripts, see CardioNER [github repo](https://github.com/DataTools4Heart/CardioNER). |
|
and for more information on the background, see Datatools4Heart [Huggingface](https://huggingface.co/DT4H)/[Website](https://www.datatools4heart.eu/) |
|
|
|
|
|
|
|
|
