End of training

README.md

@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.2513
-- Accuracy: 0.9388
-- F1: 0.9313
-- Precision: 0.9839
-- Recall: 0.8841
+- Loss: 0.2193
+- Accuracy: 0.9437
+- F1: 0.9398
+- Precision: 0.9921
+- Recall: 0.8929
 
 ## Model description
 
@@ -51,7 +51,7 @@ The following hyperparameters were used during training:
 - total_train_batch_size: 64
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- lr_scheduler_warmup_steps: 10
+- lr_scheduler_warmup_steps: 100
 - num_epochs: 5
 - mixed_precision_training: Native AMP
 
@@ -59,16 +59,16 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
-| 0.1234        | 0.97  | 9    | 0.2398          | 0.9184   | 0.9155 | 0.8904    | 0.9420 |
-| 0.1959        | 1.95  | 18   | 0.4097          | 0.8435   | 0.8535 | 0.7614    | 0.9710 |
-| 0.1138        | 2.92  | 27   | 0.4617          | 0.8639   | 0.8305 | 1.0       | 0.7101 |
-| 0.1014        | 4.0   | 37   | 0.2190          | 0.9388   | 0.9323 | 0.9688    | 0.8986 |
-| 0.0477        | 4.86  | 45   | 0.2513          | 0.9388   | 0.9313 | 0.9839    | 0.8841 |
+| 0.7008        | 0.96  | 17   | 0.6755          | 0.5704   | 0.2375 | 0.95      | 0.1357 |
+| 0.578         | 1.97  | 35   | 0.5885          | 0.6866   | 0.5822 | 0.8493    | 0.4429 |
+| 0.4858        | 2.99  | 53   | 0.4109          | 0.8239   | 0.8344 | 0.7778    | 0.9    |
+| 0.2615        | 4.0   | 71   | 0.2202          | 0.9401   | 0.9373 | 0.9695    | 0.9071 |
+| 0.1685        | 4.79  | 85   | 0.2193          | 0.9437   | 0.9398 | 0.9921    | 0.8929 |
 
 
 ### Framework versions
 
-- Transformers 4.38.2
-- Pytorch 2.1.0+cu121
+- Transformers 4.39.3
+- Pytorch 2.2.1+cu121
 - Datasets 2.18.0
 - Tokenizers 0.15.2