Instructions to use Cournane/roberta-base-labels-Upper_pattern with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cournane/roberta-base-labels-Upper_pattern with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cournane/roberta-base-labels-Upper_pattern")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Cournane/roberta-base-labels-Upper_pattern") model = AutoModelForSequenceClassification.from_pretrained("Cournane/roberta-base-labels-Upper_pattern", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Cournane/roberta-base-labels-Upper_pattern: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/Cournane/roberta-base-labels-Upper_pattern/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://Cournane/roberta-base-labels-Upper_pattern@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Cournane/roberta-base-labels-Upper_pattern/resolve/refs%2Fpr%2F1/pytorch_model.bin
499 MB
- Xet hash:
- 92d9bfd602e61cbb7abab311ca73be56871215eaa07ab168bbc940ebf263ec52
- Size of remote file:
- 499 MB
- SHA256:
- 609cef8e4b6e8ff5cdea806d242e2988a8be94580e3d22c977eb8424b72e24ce
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