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MGE-LLMs
/
SteelBERT

Fill-Mask
Transformers
PyTorch
deberta-v2
feature-extraction
Model card Files Files and versions
xet
Community
1

Instructions to use MGE-LLMs/SteelBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use MGE-LLMs/SteelBERT with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("fill-mask", model="MGE-LLMs/SteelBERT")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForMaskedLM
    
    tokenizer = AutoTokenizer.from_pretrained("MGE-LLMs/SteelBERT")
    model = AutoModelForMaskedLM.from_pretrained("MGE-LLMs/SteelBERT")
  • Notebooks
  • Google Colab
  • Kaggle

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  • .gitattributes
    1.52 kB
    initial commit over 2 years ago
  • README.md
    4.74 kB
    Update README.md about 1 year ago
  • config.json
    866 Bytes
    SteelBERT init over 2 years ago
  • optimizer.pt
    1.48 GB
    xet
    SteelBERT init over 2 years ago
  • pytorch_model.bin
    738 MB
    xet
    SteelBERT init over 2 years ago
  • rng_state.pth
    14.6 kB
    xet
    SteelBERT init over 2 years ago
  • scheduler.pt
    627 Bytes
    xet
    SteelBERT init over 2 years ago
  • special_tokens_map.json
    173 Bytes
    SteelBERT init over 2 years ago
  • tokenizer.json
    8.52 MB
    SteelBERT init over 2 years ago
  • tokenizer_config.json
    412 Bytes
    SteelBERT init over 2 years ago
  • trainer_state.json
    51.2 kB
    SteelBERT init over 2 years ago
  • training_args.bin
    3.96 kB
    xet
    SteelBERT init over 2 years ago