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xlm-roberta-large for multi-class classification of Democratic Erosion Event Dataset (DEED) events.| Label | Precision | Recall | F1-Score | Support |
|---|---|---|---|---|
| Destabilizing Event (0) | 0.598 | 0.392 | 0.473 | 125 |
| Precursor (1) | 0.659 | 0.716 | 0.686 | 675 |
| Resistance (2) | 0.846 | 0.845 | 0.846 | 645 |
| Symptom (3) | 0.800 | 0.786 | 0.793 | 805 |
| Accuracy | 0.760 | 0.760 | 0.760 | 2250 |
| Macro Avg | 0.726 | 0.685 | 0.700 | 2250 |
| Weighted Avg | 0.760 | 0.760 | 0.758 | 2250 |
1from transformers import pipeline
2
3classifier = pipeline("text-classification", model="hanifsajid/deed-v01")
4result = classifier("Example text here")
5print(result)