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README.md

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+---
+id: mirrorbert_MedRoBERTa.nl_meantoken
+name: mirrorbert_MedRoBERTa.nl_meantoken
+description: MedRoBERTa.nl continued pre-training on hard medical terms pairs from
+  the UMLS/SNOMED ontology, using the infoNCE loss function, as implemented in MirrorBERT
+license: gpl-3.0
+language: nl
+tags:
+- biology
+- embedding
+- entity linking
+- biomedical
+- science
+- bionlp
+- lexical semantic
+pipeline_tag: feature-extraction
+---
+
+# Model Card for Mirrorbert Medroberta.Nl Meantoken
+
+The model was trained on medical entity triplets (anchor, 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_meantoken
+
+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_meantoken")
+model = AutoModel.from_pretrained("UMCU/mirrorbert_MedRoBERTa.nl_meantoken").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].mean(1) 
+    all_embs.append(cls_rep.cpu().detach().numpy())
+
+all_embs = np.concatenate(all_embs, axis=0)
+```
+
+
+# Data description
+
+Hard Dutch UMLS/SNOMED synonym pairs (terms referring to the same CUI/SCUI),and including English medication names
+
+
+# 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/) 
+
+
+