Instructions to use jcblaise/bert-tagalog-base-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jcblaise/bert-tagalog-base-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jcblaise/bert-tagalog-base-uncased")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jcblaise/bert-tagalog-base-uncased") model = AutoModelForMaskedLM.from_pretrained("jcblaise/bert-tagalog-base-uncased") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 24629447ec58b7d12bec0a39cb2957046480032c1a0784819714d00bbe73d271
- Size of remote file:
- 437 MB
- SHA256:
- e42cf41db85abb1ab89d17c165ab2b10bace7575af7be6dc4d695686854f5175
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