Instructions to use Kalindu/SinBerto with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kalindu/SinBerto with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Kalindu/SinBerto")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Kalindu/SinBerto") model = AutoModelForMaskedLM.from_pretrained("Kalindu/SinBerto", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Kalindu/SinBerto: direct link, hf CLI and curl.
- Browser
- Download file 1.26 kB
-
https://huggingface.co/Kalindu/SinBerto/resolve/main/training_args.bin
- Command line
-
hf download hf://Kalindu/SinBerto/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Kalindu/SinBerto/resolve/main/training_args.bin
1.26 kB
- Xet hash:
- c9e1c81ebf371d1bf91452f9cacbb5d2821d742c941ade6ae1e0e133053f1e6b
- Size of remote file:
- 1.26 kB
- SHA256:
- b9c9584f260fb3cad859574e97347a6d93a69de6d8a1196541b5b551833e78fd
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