Training complete

README.md

@@ -21,11 +21,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0883
-- Precision: 0.7726
-- Recall: 0.7639
-- F1: 0.7682
-- Accuracy: 0.9735
+- Loss: 0.0854
+- Precision: 0.7857
+- Recall: 0.7899
+- F1: 0.7878
+- Accuracy: 0.9747
 
 ## Model description
 
@@ -51,20 +51,22 @@ The following hyperparameters were used during training:
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_ratio: 0.1
-- num_epochs: 2
+- num_epochs: 4
 - mixed_precision_training: Native AMP
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 1.0   | 231  | 0.1049          | 0.7242    | 0.7475 | 0.7356 | 0.9691   |
-| No log        | 2.0   | 462  | 0.0883          | 0.7726    | 0.7639 | 0.7682 | 0.9735   |
+| No log        | 1.0   | 231  | 0.1015          | 0.7485    | 0.7440 | 0.7462 | 0.9703   |
+| No log        | 2.0   | 462  | 0.0878          | 0.7618    | 0.7750 | 0.7684 | 0.9728   |
+| 0.2646        | 3.0   | 693  | 0.0859          | 0.7759    | 0.7912 | 0.7835 | 0.9737   |
+| 0.2646        | 4.0   | 924  | 0.0854          | 0.7857    | 0.7899 | 0.7878 | 0.9747   |
 
 
 ### Framework versions
 
 - Transformers 4.44.2
-- Pytorch 2.4.0+cu121
+- Pytorch 2.4.1+cu121
 - Datasets 2.21.0
 - Tokenizers 0.19.1