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
File size: 283 Bytes
4fd4743 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | {
"__version__": {
"sentence_transformers": "5.1.1",
"transformers": "4.57.1",
"pytorch": "2.8.0+cu126"
},
"prompts": {
"query": "",
"document": ""
},
"default_prompt_name": null,
"similarity_fn_name": "cosine",
"model_type": "SentenceTransformer"
} |