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scilons
/
SciLaD-M-custom

Fill-Mask
Transformers
PyTorch
Safetensors
English
roberta
scientific
scholarly
encoder
masked-lm
scientific-language-processing
Eval Results (legacy)
Model card Files Files and versions
xet
Community
2

Instructions to use scilons/SciLaD-M-custom with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use scilons/SciLaD-M-custom with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("fill-mask", model="scilons/SciLaD-M-custom")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForMaskedLM
    
    tokenizer = AutoTokenizer.from_pretrained("scilons/SciLaD-M-custom")
    model = AutoModelForMaskedLM.from_pretrained("scilons/SciLaD-M-custom", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
SciLaD-M-custom
2 GB
Ctrl+K
Ctrl+K
  • 3 contributors
History: 5 commits
malteos's picture
malteos
Update README.md
99faba6 verified 4 months ago
  • .gitattributes
    1.52 kB
    initial commit 10 months ago
  • README.md
    9.46 kB
    Update README.md 4 months ago
  • config.json
    637 Bytes
    Upload folder using huggingface_hub 10 months ago
  • model.safetensors
    499 MB
    xet
    Adding `safetensors` variant of this model (#1) 4 months ago
  • optimizer.pt
    998 MB
    xet
    Upload folder using huggingface_hub 10 months ago
  • pytorch_model.bin
    499 MB
    xet
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  • rng_state_0.pth
    14.5 kB
    xet
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  • rng_state_1.pth
    14.5 kB
    xet
    Upload folder using huggingface_hub 10 months ago
  • scaler.pt
    988 Bytes
    xet
    Upload folder using huggingface_hub 10 months ago
  • scheduler.pt
    1.06 kB
    xet
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  • special_tokens_map.json
    965 Bytes
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  • tokenizer.json
    3.6 MB
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  • tokenizer_config.json
    1.23 kB
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  • trainer_state.json
    396 kB
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  • training_args.bin
    5.37 kB
    xet
    Upload folder using huggingface_hub 10 months ago