Instructions to use keshan/sinhala-roberta-mc4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use keshan/sinhala-roberta-mc4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="keshan/sinhala-roberta-mc4")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("keshan/sinhala-roberta-mc4") model = AutoModelForMaskedLM.from_pretrained("keshan/sinhala-roberta-mc4") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 49c59c2ea4c40f194dd71d042b1721ec7f09094fc7f8afb0515f32a96ebfb269
- Size of remote file:
- 499 MB
- SHA256:
- 3d7d33540552555140f39418fbc999491b9a396fef8ad4910bc96707613c8872
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