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fgaim
/
tiroberta-bi-encoder

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

Instructions to use fgaim/tiroberta-bi-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use fgaim/tiroberta-bi-encoder with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("fgaim/tiroberta-bi-encoder")
    
    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 fgaim/tiroberta-bi-encoder with Transformers:

    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("fgaim/tiroberta-bi-encoder")
    model = AutoModel.from_pretrained("fgaim/tiroberta-bi-encoder")
  • Notebooks
  • Google Colab
  • Kaggle
tiroberta-bi-encoder
505 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 3 commits
fgaim's picture
fgaim
Add trained model and tokenizer
9958893 over 2 years ago
  • 1_Pooling
    Add trained model and tokenizer over 2 years ago
  • .gitattributes
    1.52 kB
    Initial setup over 2 years ago
  • .gitignore
    7 Bytes
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  • README.md
    4.47 kB
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  • config.json
    709 Bytes
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  • config_sentence_transformers.json
    171 Bytes
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  • merges.txt
    1.23 MB
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  • model.safetensors
    499 MB
    xet
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  • modules.json
    229 Bytes
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  • sentence_bert_config.json
    53 Bytes
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  • special_tokens_map.json
    965 Bytes
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  • tokenizer.json
    3.66 MB
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  • tokenizer_config.json
    1.24 kB
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  • vocab.json
    1.58 MB
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