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 pytorch_model.bin from Cournane/roberta-base-reduced-Covering: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/Cournane/roberta-base-reduced-Covering/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Cournane/roberta-base-reduced-Covering/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Cournane/roberta-base-reduced-Covering/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 41699fb846199c35c27a4ba3fa4d8ee4d8bcdfa26b0df7db6336f55ccbcda0dd
- Size of remote file:
- 499 MB
- SHA256:
- 81f84d65552b62ba2d62be1ce067e0dca788f18d1f88c6443c22455a54094c4d
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