Sentence Similarity
sentence-transformers
Safetensors
Dutch
English
xlm-roberta
medical
biology
text-embeddings-inference
Instructions to use FremyCompany/BioLORD-2023-M-Dutch-InContext-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use FremyCompany/BioLORD-2023-M-Dutch-InContext-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("FremyCompany/BioLORD-2023-M-Dutch-InContext-v1") sentences = [ "bartonellosis", "kattenkrabziekte", "wond, kattenkrab", "door teken overgedragen orbiviruskoorts", "kattenbont" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [5, 5] - Notebooks
- Google Colab
- Kaggle
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README.md
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Do mentions sharing the same text need to have the same embedding? No!
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This model supports embedding biomedical entities in both English and Dutch, but
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mention text [SEP] (context: ... a textual example containing mention text and some more text on both sides ...)
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Do mentions sharing the same text need to have the same embedding? No!
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This model supports embedding biomedical entities in both English and Dutch, but allows the in-context embedding of concepts, using the following template:
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mention text [SEP] (context: ... a textual example containing mention text and some more text on both sides ...)
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