Instructions to use xcjthu/Lawformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xcjthu/Lawformer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="xcjthu/Lawformer")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("xcjthu/Lawformer") model = AutoModelForMaskedLM.from_pretrained("xcjthu/Lawformer") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 698b31228f69d7f6e400ad983075c77a5bcbbee3a631f912e7a41b5f0b4527d3
- Size of remote file:
- 505 MB
- SHA256:
- 08d3551660a91f9c7a25f841538d47be08e8390ed937d52faf01aa0cc303afb4
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