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--- |
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id: sap_umls_MedRoBERTa.nl |
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name: sap_umls_MedRoBERTa.nl |
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description: MedRoBERTa.nl continued pre-training on hard medical terms pairs from |
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the UMLS ontology, using the multi-similarity loss function |
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license: gpl-3.0 |
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language: nl |
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tags: |
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- embedding |
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- bionlp |
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- biology |
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- science |
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- entity linking |
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- lexical semantic |
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- biomedical |
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pipeline_tag: feature-extraction |
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--- |
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# Model Card for Sap Umls Medroberta.Nl |
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The model was trained on medical entity triplets (anchor, term, synonym) |
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### Training specifics |
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``` |
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epochs : 2 |
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batch_size : 64 |
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learning_rate : 5e-6 |
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weight_decay : 1e-4 |
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max_length : 30 |
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loss : ms_loss |
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pairwise : true |
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type_of_triplets : all |
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agg_mode : CLS |
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``` |
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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 sap_umls_MedRoBERTa.nl |
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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("UMCU/sap_umls_MedRoBERTa.nl") |
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model = AutoModel.from_pretrained("UMCU/sap_umls_MedRoBERTa.nl").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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# Data description |
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Hard Dutch UMLS synonym pairs (terms referring to the same CUI). Dutch UMLS extended with matching Dutch SNOMEDCT term, and including English medication names |
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# Acknowledgement |
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This is part of the [DT4H project](https://www.datatools4heart.eu/). |
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# Doi and reference |
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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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### Citation |
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```bibtex |
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@inproceedings{liu-etal-2021-self, |
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title = "Self-Alignment Pretraining for Biomedical Entity Representations", |
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author = "Liu, Fangyu and |
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Shareghi, Ehsan and |
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Meng, Zaiqiao and |
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Basaldella, Marco and |
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Collier, Nigel", |
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booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", |
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month = jun, |
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year = "2021", |
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address = "Online", |
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publisher = "Association for Computational Linguistics", |
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url = "https://www.aclweb.org/anthology/2021.naacl-main.334", |
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pages = "4228--4238", |
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abstract = "Despite the widespread success of self-supervised learning via masked language models (MLM), accurately capturing fine-grained semantic relationships in the biomedical domain remains a challenge. This is of paramount importance for entity-level tasks such as entity linking where the ability to model entity relations (especially synonymy) is pivotal. To address this challenge, we propose SapBERT, a pretraining scheme that self-aligns the representation space of biomedical entities. We design a scalable metric learning framework that can leverage UMLS, a massive collection of biomedical ontologies with 4M+ concepts. In contrast with previous pipeline-based hybrid systems, SapBERT offers an elegant one-model-for-all solution to the problem of medical entity linking (MEL), achieving a new state-of-the-art (SOTA) on six MEL benchmarking datasets. In the scientific domain, we achieve SOTA even without task-specific supervision. With substantial improvement over various domain-specific pretrained MLMs such as BioBERT, SciBERTand and PubMedBERT, our pretraining scheme proves to be both effective and robust.", |
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} |
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``` |
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For more details about training/eval and other scripts, see CardioNER [github repo](https://github.com/DataTools4Heart/CardioNER). |
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and for more information on the background, see Datatools4Heart [Huggingface](https://huggingface.co/DT4H)/[Website](https://www.datatools4heart.eu/) |
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