Instructions to use sercetexam9/tc-xlmr-viwikifc-finetuned-vihallu-fold-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sercetexam9/tc-xlmr-viwikifc-finetuned-vihallu-fold-4 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import ClaimModelForClassification model = ClaimModelForClassification.from_pretrained("sercetexam9/tc-xlmr-viwikifc-finetuned-vihallu-fold-4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from sercetexam9/tc-xlmr-viwikifc-finetuned-vihallu-fold-4: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/sercetexam9/tc-xlmr-viwikifc-finetuned-vihallu-fold-4/resolve/main/tokenizer.json
- Command line
-
hf download hf://sercetexam9/tc-xlmr-viwikifc-finetuned-vihallu-fold-4/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/sercetexam9/tc-xlmr-viwikifc-finetuned-vihallu-fold-4/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 812880d891afa37f6d5f1e79856fb85ed0c54f91d54dfe564acbca782169c8b8
- Size of remote file:
- 17.1 MB
- SHA256:
- 6129e14723e516c4e1d6bfaedcdc91f2343e4d68200eb1a71c90a925c7f60f5d
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