Instructions to use TCMVince/HOP4NLP3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TCMVince/HOP4NLP3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="TCMVince/HOP4NLP3", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("TCMVince/HOP4NLP3", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload energybert/__init__.py with huggingface_hub
Browse files- energybert/__init__.py +6 -0
energybert/__init__.py
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from .hf_configuration import BertEnergyConfig
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from .mlm import BertEnergyModelForMaskedLM
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from .hopfield import HopfieldLayer
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from .positional import PositionalEncoding
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__all__ = ["BertEnergyConfig", "BertEnergyModelForMaskedLM", "HopfieldLayer", "PositionalEncoding"]
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