End of training

README.md

@@ -16,8 +16,8 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.2373
-- Classification Report: {'0': {'precision': 0.9255643685173887, 'recall': 0.9528894472361809, 'f1-score': 0.9390281646549056, 'support': 1592.0}, '1': {'precision': 0.647887323943662, 'recall': 0.5307692307692308, 'f1-score': 0.5835095137420718, 'support': 260.0}, 'accuracy': 0.8936285097192225, 'macro avg': {'precision': 0.7867258462305253, 'recall': 0.7418293390027058, 'f1-score': 0.7612688391984888, 'support': 1852.0}, 'weighted avg': {'precision': 0.8865816300783126, 'recall': 0.8936285097192225, 'f1-score': 0.8891173389328015, 'support': 1852.0}}
+- Loss: 0.2458
+- Classification Report: {'0': {'precision': 0.930564166150031, 'recall': 0.9428391959798995, 'f1-score': 0.9366614664586583, 'support': 1592.0}, '1': {'precision': 0.6192468619246861, 'recall': 0.5692307692307692, 'f1-score': 0.593186372745491, 'support': 260.0}, 'accuracy': 0.8903887688984882, 'macro avg': {'precision': 0.7749055140373586, 'recall': 0.7560349826053343, 'f1-score': 0.7649239196020747, 'support': 1852.0}, 'weighted avg': {'precision': 0.8868587130730388, 'recall': 0.8903887688984882, 'f1-score': 0.8884414209049739, 'support': 1852.0}}
 
 ## Model description
 
@@ -50,11 +50,11 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Classification Report                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
-|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
-| No log        | 1.0   | 25   | 0.2794          | {'0': {'precision': 0.8953152111046848, 'recall': 0.9723618090452262, 'f1-score': 0.9322493224932249, 'support': 1592.0}, '1': {'precision': 0.6422764227642277, 'recall': 0.3038461538461538, 'f1-score': 0.412532637075718, 'support': 260.0}, 'accuracy': 0.8785097192224622, 'macro avg': {'precision': 0.7687958169344562, 'recall': 0.63810398144569, 'f1-score': 0.6723909797844715, 'support': 1852.0}, 'weighted avg': {'precision': 0.8597914071260029, 'recall': 0.8785097192224622, 'f1-score': 0.8592869368514583, 'support': 1852.0}}   |
-| No log        | 2.0   | 50   | 0.2479          | {'0': {'precision': 0.921580547112462, 'recall': 0.9522613065326633, 'f1-score': 0.9366697559468644, 'support': 1592.0}, '1': {'precision': 0.6328502415458938, 'recall': 0.5038461538461538, 'f1-score': 0.5610278372591007, 'support': 260.0}, 'accuracy': 0.8893088552915767, 'macro avg': {'precision': 0.7772153943291779, 'recall': 0.7280537301894086, 'f1-score': 0.7488487966029825, 'support': 1852.0}, 'weighted avg': {'precision': 0.8810460549702872, 'recall': 0.8893088552915767, 'f1-score': 0.8839338494356234, 'support': 1852.0}} |
-| No log        | 3.0   | 75   | 0.2373          | {'0': {'precision': 0.9255643685173887, 'recall': 0.9528894472361809, 'f1-score': 0.9390281646549056, 'support': 1592.0}, '1': {'precision': 0.647887323943662, 'recall': 0.5307692307692308, 'f1-score': 0.5835095137420718, 'support': 260.0}, 'accuracy': 0.8936285097192225, 'macro avg': {'precision': 0.7867258462305253, 'recall': 0.7418293390027058, 'f1-score': 0.7612688391984888, 'support': 1852.0}, 'weighted avg': {'precision': 0.8865816300783126, 'recall': 0.8936285097192225, 'f1-score': 0.8891173389328015, 'support': 1852.0}} |
+| Training Loss | Epoch | Step | Validation Loss | Classification Report                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
+| No log        | 1.0   | 25   | 0.2760          | {'0': {'precision': 0.8786867000556483, 'recall': 0.9918341708542714, 'f1-score': 0.9318383003835939, 'support': 1592.0}, '1': {'precision': 0.7636363636363637, 'recall': 0.16153846153846155, 'f1-score': 0.26666666666666666, 'support': 260.0}, 'accuracy': 0.8752699784017278, 'macro avg': {'precision': 0.8211615318460059, 'recall': 0.5766863161963665, 'f1-score': 0.5992524835251303, 'support': 1852.0}, 'weighted avg': {'precision': 0.8625349249643881, 'recall': 0.8752699784017278, 'f1-score': 0.8384556736198784, 'support': 1852.0}} |
+| No log        | 2.0   | 50   | 0.2626          | {'0': {'precision': 0.8689240851993446, 'recall': 0.9993718592964824, 'f1-score': 0.9295939234589541, 'support': 1592.0}, '1': {'precision': 0.9523809523809523, 'recall': 0.07692307692307693, 'f1-score': 0.1423487544483986, 'support': 260.0}, 'accuracy': 0.8698704103671706, 'macro avg': {'precision': 0.9106525187901484, 'recall': 0.5381474681097796, 'f1-score': 0.5359713389536763, 'support': 1852.0}, 'weighted avg': {'precision': 0.8806404920390952, 'recall': 0.8698704103671706, 'f1-score': 0.81907354336028, 'support': 1852.0}}    |
+| No log        | 3.0   | 75   | 0.2458          | {'0': {'precision': 0.930564166150031, 'recall': 0.9428391959798995, 'f1-score': 0.9366614664586583, 'support': 1592.0}, '1': {'precision': 0.6192468619246861, 'recall': 0.5692307692307692, 'f1-score': 0.593186372745491, 'support': 260.0}, 'accuracy': 0.8903887688984882, 'macro avg': {'precision': 0.7749055140373586, 'recall': 0.7560349826053343, 'f1-score': 0.7649239196020747, 'support': 1852.0}, 'weighted avg': {'precision': 0.8868587130730388, 'recall': 0.8903887688984882, 'f1-score': 0.8884414209049739, 'support': 1852.0}}     |
 
 
 ### Framework versions

model.safetensors

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