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, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 648dc2adadfcb8f66d6dcad963a2a546db6331afff6228b42dc3287e83e6f556
- Size of remote file:
- 201 MB
- SHA256:
- 03d5ca8d5fb8ad089ef941dce630aceb768e8c556cce1279165640d0ce2b3278
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