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aekupor
/
model_utterance

Text Classification
Transformers
PyTorch
roberta
text-embeddings-inference
Model card Files Files and versions
xet
Community
1

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

  • Libraries
  • Transformers

    How to use aekupor/model_utterance with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="aekupor/model_utterance")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("aekupor/model_utterance")
    model = AutoModelForSequenceClassification.from_pretrained("aekupor/model_utterance")
  • Notebooks
  • Google Colab
  • Kaggle
model_utterance
502 MB
Ctrl+K
Ctrl+K
  • 2 contributors
History: 3 commits
aekupor's picture
aekupor
Remove code (moved to talk move handler)
6226ff4 about 3 years ago
  • __pycache__
    Add model about 3 years ago
  • .gitattributes
    1.48 kB
    initial commit about 3 years ago
  • README.md
    28 Bytes
    initial commit about 3 years ago
  • config.json
    735 Bytes
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  • eval_results.txt
    155 Bytes
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  • handler.py
    613 Bytes
    Remove code (moved to talk move handler) about 3 years ago
  • merges.txt
    456 kB
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  • model_args.json
    2.84 kB
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  • pytorch_model.bin
    499 MB
    xet
    Add model about 3 years ago
  • special_tokens_map.json
    280 Bytes
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  • test_run_handler.py
    255 Bytes
    Add model about 3 years ago
  • tokenizer.json
    2.11 MB
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  • tokenizer_config.json
    377 Bytes
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  • training_args.bin
    3.45 kB
    xet
    Add model about 3 years ago
  • training_progress_scores.csv
    1.01 kB
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  • vocab.json
    798 kB
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