Instructions to use rinna/japanese-roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rinna/japanese-roberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="rinna/japanese-roberta-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("rinna/japanese-roberta-base") model = AutoModelForMaskedLM.from_pretrained("rinna/japanese-roberta-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d229e3d5e95249409ab1ac5d46a17922d84dd7c3f7b9303d453111af61068a64
- Size of remote file:
- 443 MB
- SHA256:
- 0f8b82568ffba99a6a4ecdbec6951a64b9dd51e07fbf7b9d8f09352a54f0a668
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.