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Intel
/
xlnet-base-cased-mrpc

Text Classification
Transformers
PyTorch
English
xlnet
Generated from Trainer
Eval Results (legacy)
Model card Files Files and versions
xet
Community
3

Instructions to use Intel/xlnet-base-cased-mrpc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Intel/xlnet-base-cased-mrpc with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="Intel/xlnet-base-cased-mrpc")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("Intel/xlnet-base-cased-mrpc")
    model = AutoModelForSequenceClassification.from_pretrained("Intel/xlnet-base-cased-mrpc")
  • Notebooks
  • Google Colab
  • Kaggle
xlnet-base-cased-mrpc
473 MB
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  • 1 contributor
History: 2 commits
xinhe's picture
xinhe
upload fp32 model
686aebc about 4 years ago
  • .gitattributes
    1.22 kB
    initial commit about 4 years ago
  • README.md
    1.51 kB
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  • all_results.json
    481 Bytes
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  • config.json
    1.14 kB
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  • eval_results.json
    308 Bytes
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  • pytorch_model.bin

    Detected Pickle imports (3)

    • "torch.FloatStorage",
    • "torch._utils._rebuild_tensor_v2",
    • "collections.OrderedDict"

    What is a pickle import?

    469 MB
    xet
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  • special_tokens_map.json
    291 Bytes
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  • spiece.model
    798 kB
    xet
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  • tokenizer.json
    2.41 MB
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  • tokenizer_config.json
    489 Bytes
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  • train_results.json
    193 Bytes
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  • trainer_state.json
    838 Bytes
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  • training_args.bin

    Detected Pickle imports (6)

    • "transformers.trainer_utils.IntervalStrategy",
    • "transformers.trainer_utils.HubStrategy",
    • "transformers.trainer_utils.SchedulerType",
    • "torch.device",
    • "transformers.training_args.TrainingArguments",
    • "transformers.training_args.OptimizerNames"

    How to fix it?

    3.06 kB
    xet
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