Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing

  • Log In
  • Sign Up

TCMVince
/
HOP4NLP2

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

Instructions to use TCMVince/HOP4NLP2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use TCMVince/HOP4NLP2 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("fill-mask", model="TCMVince/HOP4NLP2", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModelForMaskedLM
    model = AutoModelForMaskedLM.from_pretrained("TCMVince/HOP4NLP2", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
HOP4NLP2 / energybert
32.7 kB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 5 commits
TCMVince's picture
TCMVince
Upload energybert/positional.py with huggingface_hub
b2c9e7c verified 29 days ago
  • __init__.py
    0 Bytes
    Upload energybert/__init__.py with huggingface_hub 29 days ago
  • hf_configuration.py
    3.25 kB
    Upload energybert/hf_configuration.py with huggingface_hub 29 days ago
  • hopfield.py
    13.4 kB
    Upload energybert/hopfield.py with huggingface_hub 29 days ago
  • mlm.py
    15 kB
    Upload energybert/mlm.py with huggingface_hub 29 days ago
  • positional.py
    1.03 kB
    Upload energybert/positional.py with huggingface_hub 29 days ago