Instructions to use alabnii/jmedroberta-base-sentencepiece with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alabnii/jmedroberta-base-sentencepiece with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="alabnii/jmedroberta-base-sentencepiece")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("alabnii/jmedroberta-base-sentencepiece") model = AutoModelForMaskedLM.from_pretrained("alabnii/jmedroberta-base-sentencepiece") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b7087962ba2dfea3e9ad4ad91d5e848d770b7808b5396d223844ccd42fb6a33b
- Size of remote file:
- 437 MB
- SHA256:
- 9f74a82d2ba05ab3ac0f60eb0bced3af7c7d0930fc0553c595878a84832c701b
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