Instructions to use uzabase/luke-japanese-wordpiece-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use uzabase/luke-japanese-wordpiece-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="uzabase/luke-japanese-wordpiece-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("uzabase/luke-japanese-wordpiece-base") model = AutoModelForMaskedLM.from_pretrained("uzabase/luke-japanese-wordpiece-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 752f5ee751106a44471fb3cd44b0a10e3b57d13b2bcc5754bd734b3cee83939c
- Size of remote file:
- 20.8 MB
- SHA256:
- 44b62a4236024bcfbc396e434fb137edecbb106e7f6bc36bc2465016d99d84dd
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