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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3113440589afcf94e353093f4ee3d671991767c7777e99fc1cf5a3e013483d80
- Size of remote file:
- 668 MB
- SHA256:
- 54c7c9416a0ffd36e918d56d67f04b4f18e3ab1a52ebfc8cd498427d67f0bd53
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