Instructions to use Cournane/roberta-base-reduced-Lower_pattern with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cournane/roberta-base-reduced-Lower_pattern with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cournane/roberta-base-reduced-Lower_pattern")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Cournane/roberta-base-reduced-Lower_pattern") model = AutoModelForSequenceClassification.from_pretrained("Cournane/roberta-base-reduced-Lower_pattern", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Cournane/roberta-base-reduced-Lower_pattern: direct link, hf CLI and curl.
- Browser
- Download file 3.96 kB
-
https://huggingface.co/Cournane/roberta-base-reduced-Lower_pattern/resolve/main/training_args.bin
- Command line
-
hf download hf://Cournane/roberta-base-reduced-Lower_pattern/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Cournane/roberta-base-reduced-Lower_pattern/resolve/main/training_args.bin
3.96 kB
- Xet hash:
- 947064282e4cb76b47999b2c63c647c2290c3cd160d3baeb7ee02951aed6e14c
- Size of remote file:
- 3.96 kB
- SHA256:
- f163686158d69cd14b5f1c731815a9f3a3b77439b1721c219f318a62d37bb4da
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