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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("keshan/sinhala-roberta-mc4") model = AutoModelForMaskedLM.from_pretrained("keshan/sinhala-roberta-mc4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from keshan/sinhala-roberta-mc4: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/keshan/sinhala-roberta-mc4/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://keshan/sinhala-roberta-mc4/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/keshan/sinhala-roberta-mc4/resolve/main/flax_model.msgpack
499 MB
- Xet hash:
- 11de8b834b7c689e01f4dc15bde8435c41783727d438568988cb9a89141d5b44
- Size of remote file:
- 499 MB
- SHA256:
- 3d998d8e6d6769b92923a4c29f68b0ea19e03e4f83e63c160d6cbe38c8b7080f
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