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Pachinee
/
sentence2vec-brd

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

Instructions to use Pachinee/sentence2vec-brd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use Pachinee/sentence2vec-brd with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("Pachinee/sentence2vec-brd")
    
    sentences = [
        "The weather is lovely today.",
        "It's so sunny outside!",
        "He drove to the stadium."
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [3, 3]
  • Transformers

    How to use Pachinee/sentence2vec-brd with Transformers:

    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("Pachinee/sentence2vec-brd")
    model = AutoModel.from_pretrained("Pachinee/sentence2vec-brd")
  • Notebooks
  • Google Colab
  • Kaggle
sentence2vec-brd
502 MB
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  • 1 contributor
History: 15 commits
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Pachinee
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  • .gitattributes
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  • README.md
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  • config.json
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  • config_sentence_transformers.json
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  • model.safetensors
    471 MB
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  • modules.json
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  • sentence_bert_config.json
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  • special_tokens_map.json
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  • tokenizer.json
    17.1 MB
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  • tokenizer_config.json
    1.42 kB
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  • unigram.json
    14.8 MB
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