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yosshstd
/
ProTrek_650M_UniRef50_protein_encoder

Sentence Similarity
sentence-transformers
Safetensors
esm
feature-extraction
dense
Model card Files Files and versions
xet
Community

Instructions to use yosshstd/ProTrek_650M_UniRef50_protein_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use yosshstd/ProTrek_650M_UniRef50_protein_encoder with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("yosshstd/ProTrek_650M_UniRef50_protein_encoder")
    
    sentences = [
        "That is a happy person",
        "That is a happy dog",
        "That is a very happy person",
        "Today is a sunny day"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
ProTrek_650M_UniRef50_protein_encoder
2.61 GB
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  • 1 contributor
History: 3 commits
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yosshstd
Update README.md
3096441 verified 11 months ago
  • 1_Pooling
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  • 2_Dense
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  • .gitattributes
    1.52 kB
    initial commit 11 months ago
  • README.md
    5.74 kB
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  • config.json
    713 Bytes
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  • config_sentence_transformers.json
    283 Bytes
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  • model.safetensors
    2.6 GB
    xet
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  • modules.json
    461 Bytes
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  • sentence_bert_config.json
    58 Bytes
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  • special_tokens_map.json
    695 Bytes
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  • tokenizer_config.json
    1.16 kB
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  • vocab.txt
    93 Bytes
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