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OneFly7
/
crossencoder_ep10_bs4_trans3

Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use OneFly7/crossencoder_ep10_bs4_trans3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use OneFly7/crossencoder_ep10_bs4_trans3 with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("OneFly7/crossencoder_ep10_bs4_trans3")
    
    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
crossencoder_ep10_bs4_trans3
1.13 GB
Ctrl+K
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  • 1 contributor
History: 2 commits
OneFly7's picture
OneFly7
Add new SentenceTransformer model
03698f4 verified over 1 year ago
  • 1_Pooling
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  • 2_Dense
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  • .gitattributes
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  • README.md
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  • config.json
    748 Bytes
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  • config_sentence_transformers.json
    202 Bytes
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  • model.safetensors
    1.11 GB
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  • modules.json
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  • sentence_bert_config.json
    53 Bytes
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  • special_tokens_map.json
    964 Bytes
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  • tokenizer.json
    17.1 MB
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  • tokenizer_config.json
    1.34 kB
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