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UMCU
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mirrorbert_medroberta.nl_clstoken

Feature Extraction
Safetensors
Dutch
roberta
biomedical
embedding
lexical semantic
entity linking
bionlp
science
biology
Model card Files Community
mirrorbert_medroberta.nl_clstoken
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---
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/)