Instructions to use Cournane/roberta-base-labels-Ring with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cournane/roberta-base-labels-Ring with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cournane/roberta-base-labels-Ring")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Cournane/roberta-base-labels-Ring") model = AutoModelForSequenceClassification.from_pretrained("Cournane/roberta-base-labels-Ring", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Cournane/roberta-base-labels-Ring: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/Cournane/roberta-base-labels-Ring/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Cournane/roberta-base-labels-Ring/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Cournane/roberta-base-labels-Ring/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 5102983bbcd566983c54bd6471d1967cd6f1cdd16b65cd2abc3299a88c13f851
- Size of remote file:
- 499 MB
- SHA256:
- 22d79116e5105a8a6b5eee16ec31e7a5bc463a91e790029d4a2d38f453f2fe35
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