Instructions to use Cournane/roberta-base-finetuned-Lower_pattern with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cournane/roberta-base-finetuned-Lower_pattern with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cournane/roberta-base-finetuned-Lower_pattern")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Cournane/roberta-base-finetuned-Lower_pattern") model = AutoModelForSequenceClassification.from_pretrained("Cournane/roberta-base-finetuned-Lower_pattern", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Cournane/roberta-base-finetuned-Lower_pattern: direct link, hf CLI and curl.
- Browser
- Download file 3.96 kB
-
https://huggingface.co/Cournane/roberta-base-finetuned-Lower_pattern/resolve/main/training_args.bin
- Command line
-
hf download hf://Cournane/roberta-base-finetuned-Lower_pattern/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Cournane/roberta-base-finetuned-Lower_pattern/resolve/main/training_args.bin
3.96 kB
- Xet hash:
- d1fa9a45dc16100f48abe396215e27772db2c0eb8e71c9af86aa0d0c555d1bec
- Size of remote file:
- 3.96 kB
- SHA256:
- 0fc31291cd28b7fc90d3ae1a300e839a938684950412f936151f4eec04b20aba
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