End of training

README.md

@@ -26,16 +26,16 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.6980249480249481
+      value: 0.7001039501039501
     - name: Precision
       type: precision
-      value: 0.6910661008340158
+      value: 0.6918647538029303
     - name: Recall
       type: recall
-      value: 0.6980249480249481
+      value: 0.7001039501039501
     - name: F1
       type: f1
-      value: 0.6930951127781922
+      value: 0.6920044305899404
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -45,11 +45,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the hatexplain dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.7560
-- Accuracy: 0.6980
-- Precision: 0.6911
-- Recall: 0.6980
-- F1: 0.6931
+- Loss: 0.7758
+- Accuracy: 0.7001
+- Precision: 0.6919
+- Recall: 0.7001
+- F1: 0.6920
 
 ## Model description
 
@@ -68,11 +68,11 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 2e-05
-- train_batch_size: 8
-- eval_batch_size: 8
+- learning_rate: 5e-05
+- train_batch_size: 16
+- eval_batch_size: 16
 - seed: 42
-- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- 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
 - num_epochs: 3
 
@@ -80,9 +80,9 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
-| 0.6426        | 1.0   | 1923 | 0.7613          | 0.6686   | 0.6613    | 0.6686 | 0.6581 |
-| 0.7264        | 2.0   | 3846 | 0.7503          | 0.6842   | 0.6784    | 0.6842 | 0.6802 |
-| 0.7134        | 3.0   | 5769 | 0.7898          | 0.6831   | 0.6770    | 0.6831 | 0.6786 |
+| 0.7621        | 1.0   | 962  | 0.7321          | 0.6805   | 0.6755    | 0.6805 | 0.6690 |
+| 0.6306        | 2.0   | 1924 | 0.7410          | 0.6863   | 0.6775    | 0.6863 | 0.6767 |
+| 0.5825        | 3.0   | 2886 | 0.7928          | 0.6868   | 0.6800    | 0.6868 | 0.6819 |
 
 
 ### Framework versions