Instructions to use Cournane/roberta-base-finetuned-Outer_pattern with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cournane/roberta-base-finetuned-Outer_pattern with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cournane/roberta-base-finetuned-Outer_pattern")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Cournane/roberta-base-finetuned-Outer_pattern") model = AutoModelForSequenceClassification.from_pretrained("Cournane/roberta-base-finetuned-Outer_pattern", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Cournane/roberta-base-finetuned-Outer_pattern: direct link, hf CLI and curl.
- Browser
- Download file 3.96 kB
-
https://huggingface.co/Cournane/roberta-base-finetuned-Outer_pattern/resolve/main/training_args.bin
- Command line
-
hf download hf://Cournane/roberta-base-finetuned-Outer_pattern/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Cournane/roberta-base-finetuned-Outer_pattern/resolve/main/training_args.bin
3.96 kB
- Xet hash:
- fbb398bda417c7913c40057df2fa9aec3408a77d7f0013c98e0f86709351121f
- Size of remote file:
- 3.96 kB
- SHA256:
- 30a8c13cb67e2c399dd9a343bd429a158807002d225706c8e76e0a1c33d2b0a6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.