Instructions to use keshan/SinhalaBERTo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use keshan/SinhalaBERTo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="keshan/SinhalaBERTo")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("keshan/SinhalaBERTo") model = AutoModelForMaskedLM.from_pretrained("keshan/SinhalaBERTo") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3f4354b8761919e6969704bbe3fe1c5b25d91ba5c9fb8a3fe46a31a064c7b6a4
- Size of remote file:
- 334 MB
- SHA256:
- 08ce65cb3c62777e3f55d7310aa347ddacedd5cd6b759a0316e13365f20ad543
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