Instructions to use jcblaise/bert-tagalog-base-uncased-WWM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jcblaise/bert-tagalog-base-uncased-WWM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jcblaise/bert-tagalog-base-uncased-WWM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jcblaise/bert-tagalog-base-uncased-WWM") model = AutoModelForMaskedLM.from_pretrained("jcblaise/bert-tagalog-base-uncased-WWM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7d0a342a39d0612c42d324e5f1dc691ff9661068017e9f602f31c4ee52a443b4
- Size of remote file:
- 439 MB
- SHA256:
- 29fa6618fab816e78c3da88a1c4278d5e0e66052a08c382a7b54416acfed69c1
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