Sentence Similarity
sentence-transformers
PyTorch
Safetensors
English
mpnet
feature-extraction
medical
biology
Instructions to use FremyCompany/BioLORD-2023-C with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use FremyCompany/BioLORD-2023-C with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("FremyCompany/BioLORD-2023-C") sentences = [ "bartonellosis", "cat scratch disease", "cat scratch wound", "tick-borne orbivirus fever", "cat fur" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [5, 5] - Inference
- Notebooks
- Google Colab
- Kaggle
Update citation
Browse files
README.md
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This model accompanies the [BioLORD-2023: Learning Ontological Representations from Definitions](https://arxiv.org/abs/2311.16075) paper. When you use this model, please cite the original paper as follows:
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```latex
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## Usage (Sentence-Transformers)
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This model accompanies the [BioLORD-2023: Learning Ontological Representations from Definitions](https://arxiv.org/abs/2311.16075) paper. When you use this model, please cite the original paper as follows:
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```latex
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@article{remy-etal-2023-biolord,
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author = {Remy, François and Demuynck, Kris and Demeester, Thomas},
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title = "{BioLORD-2023: semantic textual representations fusing large language models and clinical knowledge graph insights}",
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journal = {Journal of the American Medical Informatics Association},
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pages = {ocae029},
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year = {2024},
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month = {02},
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issn = {1527-974X},
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doi = {10.1093/jamia/ocae029},
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url = {https://doi.org/10.1093/jamia/ocae029},
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eprint = {https://academic.oup.com/jamia/advance-article-pdf/doi/10.1093/jamia/ocae029/56772025/ocae029.pdf},
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}
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```
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## Usage (Sentence-Transformers)
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