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:
- a4453a12778fe07e8446c9ae52f937680bcadcf5657a7a76f72608ef20a7464a
- Size of remote file:
- 334 MB
- SHA256:
- b85002cbabd2b300c06cf9791fa6373028ce769c7b53a1ad1631926d24a0edbf
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