Instructions to use Sami92/XLM-R-Large-Polarization-Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sami92/XLM-R-Large-Polarization-Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sami92/XLM-R-Large-Polarization-Classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sami92/XLM-R-Large-Polarization-Classifier") model = AutoModelForSequenceClassification.from_pretrained("Sami92/XLM-R-Large-Polarization-Classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b70cb676f1eeaadd2f1da0569eb2335f55d33c45d22e3b51803c3a696449a667
- Size of remote file:
- 2.24 GB
- SHA256:
- 8c187c67921533291a911fa5c09c1b9be383fdfc5b47f2a8d07087502788589c
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