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BlueAvenir
/
mentioning_type_class_model

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

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

  • Libraries
  • sentence-transformers

    How to use BlueAvenir/mentioning_type_class_model with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("BlueAvenir/mentioning_type_class_model")
    
    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]
  • Transformers

    How to use BlueAvenir/mentioning_type_class_model with Transformers:

    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("BlueAvenir/mentioning_type_class_model")
    model = AutoModel.from_pretrained("BlueAvenir/mentioning_type_class_model")
  • Notebooks
  • Google Colab
  • Kaggle
mentioning_type_class_model
1.13 GB
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  • 1 contributor
History: 2 commits
kowshikBlue's picture
kowshikBlue
Add model
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  • .gitattributes
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  • README.md
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  • config.json
    808 Bytes
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  • config_sentence_transformers.json
    118 Bytes
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  • model_head.pkl
    6.96 kB
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  • modules.json
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  • pytorch_model.bin
    1.11 GB
    xet
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  • sentence_bert_config.json
    53 Bytes
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  • sentencepiece.bpe.model
    5.07 MB
    xet
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  • special_tokens_map.json
    280 Bytes
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  • tokenizer.json
    17.1 MB
    xet
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  • tokenizer_config.json
    523 Bytes
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