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") - Inference
- Notebooks
- Google Colab
- Kaggle
Multilingual powerhouse — testing for mobile deployment
#4 opened 13 days ago
by
3morixd
TemporalMesh Transformer: 29.4 PPL at 48% compute — beats Mamba, new open-source architecture
#3 opened about 1 month ago
by
vigneshwar234