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maple
/
bert-large-cased

Token Classification
Transformers
PyTorch
bert
Model card Files Files and versions
xet
Community
1

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

  • Libraries
  • Transformers

    How to use maple/bert-large-cased with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("token-classification", model="maple/bert-large-cased")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForTokenClassification
    
    tokenizer = AutoTokenizer.from_pretrained("maple/bert-large-cased")
    model = AutoModelForTokenClassification.from_pretrained("maple/bert-large-cased")
  • Notebooks
  • Google Colab
  • Kaggle
bert-large-cased
1.33 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 4 commits
aditeyabaral's picture
aditeyabaral
add model
307bf5f over 4 years ago
  • .gitattributes
    1.18 kB
    initial commit over 4 years ago
  • config.json
    904 Bytes
    add model over 4 years ago
  • pytorch_model.bin

    Detected Pickle imports (4)

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

    What is a pickle import?

    1.33 GB
    xet
    add model over 4 years ago
  • special_tokens_map.json
    112 Bytes
    add tokenizer over 4 years ago
  • tokenizer.json
    436 kB
    add tokenizer over 4 years ago
  • tokenizer_config.json
    321 Bytes
    add tokenizer over 4 years ago
  • vocab.txt
    213 kB
    add tokenizer over 4 years ago