Instructions to use Cournane/roberta-base-reduced-Covering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cournane/roberta-base-reduced-Covering with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cournane/roberta-base-reduced-Covering")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Cournane/roberta-base-reduced-Covering") model = AutoModelForSequenceClassification.from_pretrained("Cournane/roberta-base-reduced-Covering", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Cournane/roberta-base-reduced-Covering: direct link, hf CLI and curl.
- Browser
- Download file 3.9 kB
-
https://huggingface.co/Cournane/roberta-base-reduced-Covering/resolve/main/training_args.bin
- Command line
-
hf download hf://Cournane/roberta-base-reduced-Covering/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Cournane/roberta-base-reduced-Covering/resolve/main/training_args.bin
3.9 kB
- Xet hash:
- 463e9a6844d1e5871d79a4c33b0f80a39030fa0acbaa516bf97d8f4fa7624fc9
- Size of remote file:
- 3.9 kB
- SHA256:
- 59ed73630cd6312a5c9fd4b888760d11ab0194d94de1802bf5ee685d5e5e4aea
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