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
Upload classification_report.txt
Browse files
classification_report.txt
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precision recall f1-score support
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non-polarization 0.89 0.89 0.89 1350
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polarization 0.67 0.67 0.67 463
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accuracy 0.83 1813
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macro avg 0.78 0.78 0.78 1813
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weighted avg 0.83 0.83 0.83 1813
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