Sentence Similarity
sentence-transformers
Safetensors
English
mpnet
ontology
nlp
biology
animals
fish
embedding
trait
feature-extraction
loss:CoSENTLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use imageomics/trait2vec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use imageomics/trait2vec with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("imageomics/trait2vec") sentences = [ "Ventral humeral ridge: or not", "If metasternum ossified, shape: long, narrow and tapering markedly anteriorly to posteriorly, length up to 3.5 times maximum width", "Astragalus, dorsolateral margin:: overlaps the anterior and posterior portions of the calcaneum equally", "Ulna size: does not apply" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Update bibtex reference of Sentence-BERT
Browse files
README.md
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
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author = "Reimers, Nils and Gurevych, Iryna",
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
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month = "
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year = "2019",
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pages = "
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publisher = "Association for Computational Linguistics",
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doi = "10.18653/v1/D19-1410"
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}
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```
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
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author = "Reimers, Nils and Gurevych, Iryna",
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
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month = "11",
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year = "2019",
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pages = "3982-3992",
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publisher = "Association for Computational Linguistics",
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url = "https://aclanthology.org/D19-1410/",
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doi = "10.18653/v1/D19-1410"
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}
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```
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