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 pytorch_model.bin from Cournane/roberta-base-labels-Covering: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/Cournane/roberta-base-labels-Covering/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Cournane/roberta-base-labels-Covering/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Cournane/roberta-base-labels-Covering/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 5ad2fef91362826061386799aba6d73e68048bc3db7071fb2edd278a391f9477
- Size of remote file:
- 499 MB
- SHA256:
- cbbd4da6a15b2611c8b8b762c3863c2431626585c33434e52c7ce428ebd2ddac
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.