Instructions to use jcblaise/bert-tagalog-base-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jcblaise/bert-tagalog-base-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jcblaise/bert-tagalog-base-cased")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jcblaise/bert-tagalog-base-cased") model = AutoModelForMaskedLM.from_pretrained("jcblaise/bert-tagalog-base-cased") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0b5d06c72d08463bf32b2d4280e42d34e7536e567b478acafe018d037d9a38dc
- Size of remote file:
- 437 MB
- SHA256:
- 3048d3aeaac5b8838daa05fe8a0dce7eec0e973ff55fd361c8ac1c8fbc9a398f
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