End of training

README.md

@@ -22,16 +22,16 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.9298245614035088
+      value: 0.8972431077694235
     - name: Precision
       type: precision
-      value: 0.9292447472185437
+      value: 0.8989153352434833
     - name: Recall
       type: recall
-      value: 0.9298245614035088
+      value: 0.8972431077694235
     - name: F1
       type: f1
-      value: 0.9293437948869628
+      value: 0.8974179462177999
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -41,11 +41,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.2288
-- Accuracy: 0.9298
-- Precision: 0.9292
-- Recall: 0.9298
-- F1: 0.9293
+- Loss: 0.3892
+- Accuracy: 0.8972
+- Precision: 0.8989
+- Recall: 0.8972
+- F1: 0.8974
 
 ## Model description
 
@@ -73,22 +73,19 @@ 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: 10
+- num_epochs: 7
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
-| 2.2522        | 0.98  | 49   | 1.6370          | 0.6090   | 0.6189    | 0.6090 | 0.5764 |
-| 1.2901        | 1.98  | 99   | 0.9974          | 0.7556   | 0.7655    | 0.7556 | 0.7426 |
-| 1.0046        | 2.99  | 149  | 0.6645          | 0.8195   | 0.8226    | 0.8195 | 0.8162 |
-| 0.5952        | 3.99  | 199  | 0.5054          | 0.8459   | 0.8561    | 0.8459 | 0.8460 |
-| 0.3596        | 4.99  | 249  | 0.3729          | 0.9023   | 0.9117    | 0.9023 | 0.9041 |
-| 0.2534        | 5.99  | 299  | 0.2953          | 0.9073   | 0.9088    | 0.9073 | 0.9075 |
-| 0.1413        | 7.0   | 349  | 0.2545          | 0.9223   | 0.9229    | 0.9223 | 0.9216 |
-| 0.0759        | 8.0   | 399  | 0.2593          | 0.9198   | 0.9209    | 0.9198 | 0.9190 |
-| 0.0491        | 8.98  | 448  | 0.2288          | 0.9298   | 0.9292    | 0.9298 | 0.9293 |
-| 0.0355        | 9.82  | 490  | 0.2392          | 0.9223   | 0.9231    | 0.9223 | 0.9221 |
+| 2.2319        | 0.98  | 49   | 1.5808          | 0.5263   | 0.5682    | 0.5263 | 0.4767 |
+| 1.2682        | 1.98  | 99   | 0.9750          | 0.7556   | 0.7524    | 0.7556 | 0.7510 |
+| 0.9462        | 2.99  | 149  | 0.7403          | 0.7945   | 0.7964    | 0.7945 | 0.7921 |
+| 0.5946        | 3.99  | 199  | 0.5921          | 0.8233   | 0.8281    | 0.8233 | 0.8214 |
+| 0.4095        | 4.99  | 249  | 0.4772          | 0.8634   | 0.8663    | 0.8634 | 0.8638 |
+| 0.3349        | 5.99  | 299  | 0.4167          | 0.8835   | 0.8866    | 0.8835 | 0.8841 |
+| 0.2427        | 6.88  | 343  | 0.3892          | 0.8972   | 0.8989    | 0.8972 | 0.8974 |
 
 
 ### Framework versions