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inesctec
/
CitiLink-BERTimbau-large-Topic-Classification-pt

Text Classification
Transformers
Joblib
Safetensors
Portuguese
bert
multi-label-classification
bertimbau
portuguese
municipal-documents
meeting-minutes
fine-tuned
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use inesctec/CitiLink-BERTimbau-large-Topic-Classification-pt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use inesctec/CitiLink-BERTimbau-large-Topic-Classification-pt with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="inesctec/CitiLink-BERTimbau-large-Topic-Classification-pt")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("inesctec/CitiLink-BERTimbau-large-Topic-Classification-pt")
    model = AutoModelForSequenceClassification.from_pretrained("inesctec/CitiLink-BERTimbau-large-Topic-Classification-pt")
  • Notebooks
  • Google Colab
  • Kaggle
CitiLink-BERTimbau-large-Topic-Classification-pt
1.34 GB
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  • 1 contributor
History: 4 commits
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jmisidro
Update README.md
9a32b1a verified about 1 month ago
  • .gitattributes
    1.52 kB
    initial commit about 1 month ago
  • LICENSE
    36.7 kB
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  • README.md
    6.42 kB
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  • config.json
    1.79 kB
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  • gitattributes
    1.52 kB
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  • mlb_encoder.joblib
    1.6 kB
    xet
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  • model.safetensors
    1.34 GB
    xet
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  • optimal_thresholds.npy
    304 Bytes
    xet
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  • requirements.txt
    45 Bytes
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  • special_tokens_map.json
    125 Bytes
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  • tokenizer.json
    678 kB
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  • tokenizer_config.json
    1.3 kB
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  • training_args.bin
    5.71 kB
    xet
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  • vocab.txt
    210 kB
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