Instructions to use jcblaise/roberta-tagalog-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jcblaise/roberta-tagalog-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jcblaise/roberta-tagalog-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jcblaise/roberta-tagalog-base") model = AutoModelForMaskedLM.from_pretrained("jcblaise/roberta-tagalog-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0ed58eb35d2c345df2e97e32cc7b6358d70cdd1aca131eb613c67b46a7f40313
- Size of remote file:
- 437 MB
- SHA256:
- fb3ac46c3b190a6753f17359380923e70a8eff05eaa17c1ebf20f7e216110236
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.