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CALDISS-AAU
/
da-reported-speech-e5

Text Classification
setfit
Safetensors
sentence-transformers
Danish
Few-Shot
Transformers
Text-classification
generated_from_setfit_traine
Computational_humanities
SSH
Social-work
Model card Files Files and versions
xet
Community

Instructions to use CALDISS-AAU/da-reported-speech-e5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • setfit

    How to use CALDISS-AAU/da-reported-speech-e5 with setfit:

    from setfit import SetFitModel
    
    model = SetFitModel.from_pretrained("CALDISS-AAU/da-reported-speech-e5")
  • sentence-transformers

    How to use CALDISS-AAU/da-reported-speech-e5 with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("CALDISS-AAU/da-reported-speech-e5")
    
    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
da-reported-speech-e5 / rep_speech_model
2.26 GB
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  • 1 contributor
History: 1 commit
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SirMappel
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7be0c1e verified about 1 year ago
  • 1_Pooling
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  • README.md
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  • config.json
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  • config_sentence_transformers.json
    201 Bytes
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  • config_setfit.json
    103 Bytes
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  • model.safetensors
    2.24 GB
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  • model_head.pkl

    Detected Pickle imports (4)

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

    How to fix it?

    9.2 kB
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  • modules.json
    349 Bytes
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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
    xet
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  • tokenizer_config.json
    1.34 kB
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