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
| Telegram Testset | |
| precision recall f1-score support | |
| factual 0.88 0.92 0.90 71 | |
| non-factual 0.92 0.88 0.90 78 | |
| accuracy 0.90 149 | |
| macro avg 0.90 0.90 0.90 149 | |
| weighted avg 0.90 0.90 0.90 149 | |
| GermEval21 (Facebook Comments) | |
| accuracy = .79 | |