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 pytorch_model.bin from Cournane/roberta-base-finetuned-Lower_pattern: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/Cournane/roberta-base-finetuned-Lower_pattern/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Cournane/roberta-base-finetuned-Lower_pattern/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Cournane/roberta-base-finetuned-Lower_pattern/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 838f0b24bc57aa1e75daaf88d15b7a5c0c48bc70fdd89d695972cc505b6079ca
- Size of remote file:
- 499 MB
- SHA256:
- bca7e907f947983e4e1c4fb6c4382938f6434d2265953d62108d75fd24e3070e
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