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:
- 77be772056cf8ae9e742db153d401b9f175ddcd00882cf4761d087cc69da00de
- Size of remote file:
- 1.34 GB
- SHA256:
- c84f3efa6e2a9c61992ce01f1300df374da19c3c257ce40a6f235872a4d37162
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