Instructions to use Cournane/roberta-base-labels-Covering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cournane/roberta-base-labels-Covering with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cournane/roberta-base-labels-Covering")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Cournane/roberta-base-labels-Covering") model = AutoModelForSequenceClassification.from_pretrained("Cournane/roberta-base-labels-Covering", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Cournane/roberta-base-labels-Covering: direct link, hf CLI and curl.
- Browser
- Download file 3.9 kB
-
https://huggingface.co/Cournane/roberta-base-labels-Covering/resolve/main/training_args.bin
- Command line
-
hf download hf://Cournane/roberta-base-labels-Covering/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Cournane/roberta-base-labels-Covering/resolve/main/training_args.bin
3.9 kB
- Xet hash:
- f6bbcbae850be1f6a9bc35abab28f978486e3d9670171c2280309fb9f8588a35
- Size of remote file:
- 3.9 kB
- SHA256:
- 50cc86e774e8c80401e1fd5f01c1d81d1782ad0c9daca49a79bfd87759408b80
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