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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload Scores.txt
Browse files- Scores.txt +12 -0
Scores.txt
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Telegram Testset
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precision recall f1-score support
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factual 0.88 0.92 0.90 71
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non-factual 0.92 0.88 0.90 78
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accuracy 0.90 149
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macro avg 0.90 0.90 0.90 149
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weighted avg 0.90 0.90 0.90 149
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GermEval21 (Facebook Comments)
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accuracy = .79
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