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:
- 31c667e9772a1e2e968e538cc9dec2c41cfc89bb2d5ceeee5323207f7397b8e9
- Size of remote file:
- 23.7 MB
- SHA256:
- be6327e7cafc2f2b5f694a594d57113fd2bf6b620c592929202f75683b18b67d
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