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NLBSE
/
nlbse26_java

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use NLBSE/nlbse26_java with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • setfit

    How to use NLBSE/nlbse26_java with setfit:

    from setfit import SetFitModel
    
    model = SetFitModel.from_pretrained("NLBSE/nlbse26_java")
  • sentence-transformers

    How to use NLBSE/nlbse26_java with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("NLBSE/nlbse26_java")
    
    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]
  • Notebooks
  • Google Colab
  • Kaggle
nlbse26_java
91.8 MB
Ctrl+K
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  • 1 contributor
History: 4 commits
mmock's picture
mmock
Update README.md
0df98d0 verified 9 months ago
  • 1_Pooling
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  • .gitattributes
    1.52 kB
    initial commit 9 months ago
  • README.md
    3.88 kB
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  • config.json
    617 Bytes
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  • config_sentence_transformers.json
    283 Bytes
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  • config_setfit.json
    53 Bytes
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  • model.safetensors
    90.9 MB
    xet
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  • model_head.pkl

    Detected Pickle imports (5)

    • "numpy.dtype",
    • "joblib.numpy_pickle.NumpyArrayWrapper",
    • "sklearn.multioutput.MultiOutputClassifier",
    • "sklearn.linear_model._logistic.LogisticRegression",
    • "numpy.ndarray"

    How to fix it?

    24.9 kB
    xet
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  • modules.json
    229 Bytes
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  • sentence_bert_config.json
    57 Bytes
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  • special_tokens_map.json
    695 Bytes
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  • tokenizer.json
    712 kB
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  • tokenizer_config.json
    1.27 kB
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  • vocab.txt
    232 kB
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