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TCMVince
/
HOP4NLP

Fill-Mask
Transformers
Safetensors
bert_energy
custom_code
Model card Files Files and versions
xet
Community

Instructions to use TCMVince/HOP4NLP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use TCMVince/HOP4NLP with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("fill-mask", model="TCMVince/HOP4NLP", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModelForMaskedLM
    model = AutoModelForMaskedLM.from_pretrained("TCMVince/HOP4NLP", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
HOP4NLP
201 MB
Ctrl+K
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  • 1 contributor
History: 6 commits
TCMVince's picture
TCMVince
Upload positional.py with huggingface_hub
0efcf9c verified 28 days ago
  • .gitattributes
    1.52 kB
    initial commit 28 days ago
  • README.md
    5.17 kB
    Upload model 28 days ago
  • config.json
    901 Bytes
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  • hf_configuration.py
    3.25 kB
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  • hopfield.py
    13.4 kB
    Upload hopfield.py with huggingface_hub 28 days ago
  • mlm.py
    15 kB
    Upload model 28 days ago
  • model.safetensors
    201 MB
    xet
    Upload model 28 days ago
  • positional.py
    1.03 kB
    Upload positional.py with huggingface_hub 28 days ago
  • special_tokens_map.json
    695 Bytes
    Upload folder using huggingface_hub 28 days ago
  • tokenizer_config.json
    1.16 kB
    Upload folder using huggingface_hub 28 days ago