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Cheng98
/
opt-125m-rte

Text Classification
Transformers
PyTorch
English
opt
Generated from Trainer
Eval Results (legacy)
Model card Files Files and versions
xet
Community
1

Instructions to use Cheng98/opt-125m-rte with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Cheng98/opt-125m-rte with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="Cheng98/opt-125m-rte")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("Cheng98/opt-125m-rte")
    model = AutoModelForSequenceClassification.from_pretrained("Cheng98/opt-125m-rte")
  • Notebooks
  • Google Colab
  • Kaggle
opt-125m-rte
504 MB
Ctrl+K
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  • 1 contributor
History: 6 commits
Cheng98's picture
Cheng98
Upload folder using huggingface_hub
5cb7188 verified about 2 years ago
  • .gitattributes
    1.52 kB
    initial commit about 2 years ago
  • .gitignore
    13 Bytes
    Model save about 2 years ago
  • README.md
    1.42 kB
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  • all_results.json
    400 Bytes
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  • config.json
    974 Bytes
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  • eval_results.json
    227 Bytes
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  • merges.txt
    456 kB
    Model save about 2 years ago
  • pytorch_model.bin
    501 MB
    xet
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  • special_tokens_map.json
    548 Bytes
    Model save about 2 years ago
  • tokenizer.json
    2.11 MB
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  • tokenizer_config.json
    870 Bytes
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  • train_results.json
    193 Bytes
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  • trainer_state.json
    710 Bytes
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  • training_args.bin
    3.96 kB
    xet
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  • vocab.json
    798 kB
    Model save about 2 years ago