Instructions to use Cournane/roberta-base-finetuned-Upper_pattern with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cournane/roberta-base-finetuned-Upper_pattern with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cournane/roberta-base-finetuned-Upper_pattern")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Cournane/roberta-base-finetuned-Upper_pattern") model = AutoModelForSequenceClassification.from_pretrained("Cournane/roberta-base-finetuned-Upper_pattern", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Cournane/roberta-base-finetuned-Upper_pattern: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/Cournane/roberta-base-finetuned-Upper_pattern/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Cournane/roberta-base-finetuned-Upper_pattern/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Cournane/roberta-base-finetuned-Upper_pattern/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 668f295af94fc43c772c5f466d1f366d0aaef8332b178a3bc3be0e87e2ef0b67
- Size of remote file:
- 499 MB
- SHA256:
- fc5ee2f3fca62b202c3677bb027c88eb71343a8d2a32bc00ffc8c71b8fefd0a7
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