Sentence Similarity
sentence-transformers
PyTorch
Safetensors
English
mpnet
feature-extraction
medical
biology
text-embeddings-inference
Instructions to use FremyCompany/BioLORD-2023 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use FremyCompany/BioLORD-2023 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("FremyCompany/BioLORD-2023") 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
Commit ·
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## Citation
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This model accompanies the [BioLORD-2023: Learning Ontological Representations from Definitions](https://arxiv.org/abs/
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```latex
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@inproceedings{remy-et-al-2023-biolord,
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author = "Remy, François and
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Demuynck, Kris and
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Demeester, Thomas",
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month =
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year = "2023",
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}
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```
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## Usage (Sentence-Transformers)
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## Citation
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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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@inproceedings{remy-et-al-2023-biolord,
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author = "Remy, François and
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Demuynck, Kris and
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Demeester, Thomas",
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month = nov,
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year = "2023",
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eprint={2311.16075},
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archivePrefix={arXiv},
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primaryClass={cs.CL}}
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```
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## Usage (Sentence-Transformers)
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