Instructions to use jcblaise/roberta-tagalog-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jcblaise/roberta-tagalog-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jcblaise/roberta-tagalog-large")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jcblaise/roberta-tagalog-large") model = AutoModelForMaskedLM.from_pretrained("jcblaise/roberta-tagalog-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0bb90336a743515510ae347882b05ec0b0dc2300a8163fa2ec66dc51850b520c
- Size of remote file:
- 1.46 GB
- SHA256:
- 74f2d81ba05e8440cd113c070310b4f38300512b804f9e2de274800e835a821f
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