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
| from .hf_configuration import BertEnergyConfig | |
| from .mlm import BertEnergyModelForMaskedLM | |
| from .hopfield import HopfieldLayer | |
| from .positional import PositionalEncoding | |
| __all__ = ["BertEnergyConfig", "BertEnergyModelForMaskedLM", "HopfieldLayer", "PositionalEncoding"] | |