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MLRS
/
BERTu_upos

Token Classification
Transformers
TensorBoard
Safetensors
Maltese
bert
Eval Results (legacy)
Model card Files Files and versions
xet
Metrics Training metrics Community

Instructions to use MLRS/BERTu_upos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use MLRS/BERTu_upos with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("token-classification", model="MLRS/BERTu_upos")
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForTokenClassification
    
    tokenizer = AutoTokenizer.from_pretrained("MLRS/BERTu_upos")
    model = AutoModelForTokenClassification.from_pretrained("MLRS/BERTu_upos", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle

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Preview of files found in this repository
  • config.json
    1.25 kB
    Label mapping configuration fix. about 1 year ago
  • model.safetensors
    502 MB
    xet
    Model upload. about 1 year ago
  • optimizer.pt
    1 GB
    xet
    Model upload. about 1 year ago
  • rng_state.pth
    14.6 kB
    xet
    Model upload. about 1 year ago
  • scheduler.pt
    1.47 kB
    xet
    Model upload. about 1 year ago
  • special_tokens_map.json
    695 Bytes
    Model upload. about 1 year ago
  • tokenizer.json
    1.22 MB
    Label mapping configuration fix. about 1 year ago
  • tokenizer_config.json
    1.35 kB
    Model upload. about 1 year ago
  • trainer_state.json
    88.5 kB
    Label mapping configuration fix. about 1 year ago
  • training_args.bin
    5.91 kB
    xet
    Label mapping configuration fix. about 1 year ago
  • vocab.txt
    401 kB
    Model upload. about 1 year ago