Instructions to use ductuan024/AimeLaw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ductuan024/AimeLaw with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ductuan024/AimeLaw")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ductuan024/AimeLaw") model = AutoModelForMaskedLM.from_pretrained("ductuan024/AimeLaw", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c46b834285a1e66fe30c86b9d17b43003e80636500a7f35c521a4252ddbec740
- Size of remote file:
- 535 MB
- SHA256:
- a686239ba8dc0898022956b1c8b147ffbcb4b22b5f425c41934d642de9e787bf
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