End of training

README.md

@@ -19,9 +19,9 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.6070
-- Accuracy: 0.9299
-- F1: 0.9302
+- Loss: 0.6234
+- Accuracy: 0.9414
+- F1: 0.9411
 
 ## Model description
 
@@ -40,9 +40,9 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 0.0001
-- train_batch_size: 256
-- eval_batch_size: 256
+- learning_rate: 8e-05
+- train_batch_size: 192
+- eval_batch_size: 192
 - seed: 42
 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
 - lr_scheduler_type: linear
@@ -53,20 +53,25 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     |
 |:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
-| 1.6137        | 0.3460 | 100  | 0.6508          | 0.7896   | 0.7990 |
-| 0.7728        | 0.6920 | 200  | 0.5313          | 0.8152   | 0.8313 |
-| 0.5905        | 1.0381 | 300  | 0.5167          | 0.8502   | 0.8655 |
-| 0.5345        | 1.3841 | 400  | 0.4394          | 0.8721   | 0.8786 |
-| 0.4333        | 1.7301 | 500  | 0.3986          | 0.8704   | 0.8764 |
-| 0.4229        | 2.0761 | 600  | 0.4473          | 0.9003   | 0.9005 |
-| 0.2329        | 2.4221 | 700  | 0.3805          | 0.8971   | 0.9000 |
-| 0.2324        | 2.7682 | 800  | 0.4140          | 0.9058   | 0.9069 |
-| 0.1957        | 3.1142 | 900  | 0.6101          | 0.9235   | 0.9232 |
-| 0.0923        | 3.4602 | 1000 | 0.4950          | 0.9206   | 0.9218 |
-| 0.0976        | 3.8062 | 1100 | 0.5512          | 0.9307   | 0.9304 |
-| 0.0621        | 4.1522 | 1200 | 0.5888          | 0.9299   | 0.9307 |
-| 0.037         | 4.4983 | 1300 | 0.6032          | 0.9287   | 0.9290 |
-| 0.0379        | 4.8443 | 1400 | 0.6070          | 0.9299   | 0.9302 |
+| 1.7318        | 0.2591 | 100  | 0.8266          | 0.6828   | 0.7221 |
+| 0.8015        | 0.5181 | 200  | 0.5297          | 0.8188   | 0.8271 |
+| 0.6969        | 0.7772 | 300  | 0.4705          | 0.8342   | 0.8482 |
+| 0.5794        | 1.0363 | 400  | 0.4594          | 0.8686   | 0.8714 |
+| 0.4761        | 1.2953 | 500  | 0.4386          | 0.8736   | 0.8777 |
+| 0.4406        | 1.5544 | 600  | 0.4049          | 0.8858   | 0.8874 |
+| 0.4279        | 1.8135 | 700  | 0.4328          | 0.8955   | 0.8965 |
+| 0.4206        | 2.0725 | 800  | 0.4511          | 0.9006   | 0.9011 |
+| 0.2375        | 2.3316 | 900  | 0.4064          | 0.9018   | 0.9034 |
+| 0.1855        | 2.5907 | 1000 | 0.4421          | 0.9099   | 0.9109 |
+| 0.1951        | 2.8497 | 1100 | 0.3979          | 0.9140   | 0.9150 |
+| 0.1464        | 3.1088 | 1200 | 0.5462          | 0.9253   | 0.9253 |
+| 0.0533        | 3.3679 | 1300 | 0.5703          | 0.9313   | 0.9314 |
+| 0.0508        | 3.6269 | 1400 | 0.5185          | 0.9343   | 0.9342 |
+| 0.0488        | 3.8860 | 1500 | 0.5403          | 0.9378   | 0.9375 |
+| 0.0268        | 4.1451 | 1600 | 0.5958          | 0.9399   | 0.9396 |
+| 0.007         | 4.4041 | 1700 | 0.5955          | 0.9379   | 0.9377 |
+| 0.0052        | 4.6632 | 1800 | 0.6330          | 0.9400   | 0.9397 |
+| 0.0049        | 4.9223 | 1900 | 0.6234          | 0.9414   | 0.9411 |
 
 
 ### Framework versions