Instructions to use jcblaise/roberta-tagalog-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jcblaise/roberta-tagalog-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jcblaise/roberta-tagalog-small")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jcblaise/roberta-tagalog-small") model = AutoModelForMaskedLM.from_pretrained("jcblaise/roberta-tagalog-small") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4abb10c256240094f528bedef390311c787d1191d97e481212f85ab15480dba1
- Size of remote file:
- 273 MB
- SHA256:
- cc999aca18f2c135590fb9cf34ff9d37b9e9319e9ee5683fea74a21045a601e3
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