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VMware
/
bert-large-mrqa

Question Answering
Transformers
PyTorch
Safetensors
English
bert
Eval Results (legacy)
Model card Files Files and versions
xet
Community
1

Instructions to use VMware/bert-large-mrqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use VMware/bert-large-mrqa with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("question-answering", model="VMware/bert-large-mrqa")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForQuestionAnswering
    
    tokenizer = AutoTokenizer.from_pretrained("VMware/bert-large-mrqa")
    model = AutoModelForQuestionAnswering.from_pretrained("VMware/bert-large-mrqa")
  • Notebooks
  • Google Colab
  • Kaggle
bert-large-mrqa
2.67 GB
Ctrl+K
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  • 4 contributors
History: 6 commits
Teja-Gollapudi's picture
Teja-Gollapudi
SFconvertbot's picture
SFconvertbot
Adding `safetensors` variant of this model (#1)
340abc2 almost 3 years ago
  • .gitattributes
    1.48 kB
    initial commit about 3 years ago
  • README.md
    6.22 kB
    Update README.md about 3 years ago
  • config.json
    675 Bytes
    Upload 6 files about 3 years ago
  • model.safetensors
    1.34 GB
    xet
    Adding `safetensors` variant of this model (#1) almost 3 years ago
  • pytorch_model.bin
    1.34 GB
    xet
    Upload 6 files about 3 years ago
  • special_tokens_map.json
    125 Bytes
    Upload 6 files about 3 years ago
  • tokenizer.json
    711 kB
    Upload 6 files about 3 years ago
  • tokenizer_config.json
    349 Bytes
    Upload 6 files about 3 years ago
  • vocab.txt
    232 kB
    Upload 6 files about 3 years ago