Instructions to use Sami92/XLM-R-Large-ClaimDetection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sami92/XLM-R-Large-ClaimDetection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sami92/XLM-R-Large-ClaimDetection")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sami92/XLM-R-Large-ClaimDetection") model = AutoModelForSequenceClassification.from_pretrained("Sami92/XLM-R-Large-ClaimDetection") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 442576d463fe188f2cdf20ef02dbcd8c4bdbf6e16f72e645c31f8e75966b0e41
- Size of remote file:
- 2.24 GB
- SHA256:
- fab191aa97594a106540643d5abe57cd27465f3af83b8bf241a20c684b95f6a7
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