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
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# Model Card for Trait2Vec
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Trait2Vec is a
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Through qualitative data exploration we observe the cosine similarity between embeddings of raw trait description is proportional to the semantic similarity of their corresponding ontological representations.
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## Model Details
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# Model Card for Trait2Vec
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Trait2Vec is a language model to embed organismal trait descriptions in a way that preserves the structure induced by a semantic similarity metric (e.g. SimGIC). The model was trained on the [char-sim-data](https://huggingface.co/datasets/imageomics/char-sim-data/edit/main/README.md).
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Through qualitative data exploration we observe the cosine similarity between embeddings of raw trait description is proportional to the semantic similarity of their corresponding ontological representations.
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## Model Details
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