End of training

README.md

@@ -20,14 +20,14 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the generator dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.4935
-- Accuracy: 0.9044
-- Micro Precision: 0.9044
-- Micro Recall: 0.9044
-- Micro F1: 0.9044
-- Macro Precision: 0.8822
-- Macro Recall: 0.8527
-- Macro F1: 0.8599
+- Loss: 3.4922
+- Accuracy: 0.0913
+- Micro Precision: 0.0913
+- Micro Recall: 0.0913
+- Micro F1: 0.0913
+- Macro Precision: 0.0017
+- Macro Recall: 0.0162
+- Macro F1: 0.0030
 
 ## Model description
 
@@ -46,7 +46,7 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 1e-05
+- learning_rate: 0.0001
 - train_batch_size: 12
 - eval_batch_size: 12
 - seed: 42
@@ -55,17 +55,16 @@ The following hyperparameters were used during training:
 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_steps: 500
-- num_epochs: 5
+- num_epochs: 4
 
 ### Training results
 
 | Training Loss | Epoch  | Step   | Validation Loss | Accuracy | Micro Precision | Micro Recall | Micro F1 | Macro Precision | Macro Recall | Macro F1 |
 |:-------------:|:------:|:------:|:---------------:|:--------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|
-| 0.3304        | 1.0000 | 40166  | 0.3911          | 0.8883   | 0.8883          | 0.8883       | 0.8883   | 0.8651          | 0.8285       | 0.8359   |
-| 0.2376        | 2.0    | 80333  | 0.3998          | 0.9014   | 0.9014          | 0.9014       | 0.9014   | 0.8714          | 0.8480       | 0.8523   |
-| 0.1733        | 3.0000 | 120499 | 0.4297          | 0.9059   | 0.9059          | 0.9059       | 0.9059   | 0.8718          | 0.8607       | 0.8610   |
-| 0.1268        | 4.0    | 160666 | 0.4822          | 0.9088   | 0.9088          | 0.9088       | 0.9088   | 0.8807          | 0.8608       | 0.8649   |
-| 0.1038        | 4.9999 | 200830 | 0.5242          | 0.9087   | 0.9087          | 0.9087       | 0.9087   | 0.8753          | 0.8646       | 0.8660   |
+| 3.3304        | 1.0000 | 40166  | 3.4135          | 0.0955   | 0.0955          | 0.0955       | 0.0955   | 0.0035          | 0.0183       | 0.0048   |
+| 3.3           | 2.0    | 80333  | 5.1442          | 0.0124   | 0.0124          | 0.0124       | 0.0124   | 0.0002          | 0.0156       | 0.0004   |
+| 3.2756        | 3.0000 | 120499 | 4.6440          | 0.0124   | 0.0124          | 0.0124       | 0.0124   | 0.0002          | 0.0156       | 0.0004   |
+| 3.2737        | 4.0000 | 160664 | 5.3371          | 0.0124   | 0.0124          | 0.0124       | 0.0124   | 0.0002          | 0.0156       | 0.0004   |
 
 
 ### Framework versions

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