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vuiseng9
/
bert-base-squadv1

Question Answering
Transformers
PyTorch
ONNX
bert
Model card Files Files and versions
xet
Community
1

Instructions to use vuiseng9/bert-base-squadv1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use vuiseng9/bert-base-squadv1 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("question-answering", model="vuiseng9/bert-base-squadv1")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForQuestionAnswering
    
    tokenizer = AutoTokenizer.from_pretrained("vuiseng9/bert-base-squadv1")
    model = AutoModelForQuestionAnswering.from_pretrained("vuiseng9/bert-base-squadv1", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
bert-base-squadv1
1.36 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 5 commits
Chua, Vui Seng
Add OpenVINO IR
eab1115 over 4 years ago
  • ir
    Add OpenVINO IR over 4 years ago
  • .gitattributes
    1.31 kB
    Add OpenVINO IR over 4 years ago
  • README.md
    676 Bytes
    Add collaterals over 4 years ago
  • all_results.json
    106 Bytes
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  • bert-base-squadv1.onnx
    436 MB
    xet
    Update bert-base-squadv1.onnx generated with torch 1.8.1 over 4 years ago
  • config.json
    477 Bytes
    Add model over 4 years ago
  • eval_nbest_predictions.json
    49.5 MB
    xet
    Add collaterals over 4 years ago
  • eval_predictions.json
    592 kB
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  • eval_results.json
    106 Bytes
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  • layer_wise_sparsity_global_rate_0.00.csv
    16.2 kB
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  • layer_wise_sparsity_global_rate_0.00.md
    27.9 kB
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  • linear_layer_sparsity_85M_params_0.00_sparsity.csv
    6.54 kB
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  • linear_layer_sparsity_85M_params_0.00_sparsity.md
    9.84 kB
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  • pytorch_model.bin
    438 MB
    xet
    Add model over 4 years ago
  • special_tokens_map.json
    112 Bytes
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  • tokenizer_config.json
    48 Bytes
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  • training_args.bin
    1.64 kB
    xet
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  • vocab.txt
    232 kB
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