End of training

README.md

@@ -23,7 +23,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.78
+      value: 0.87
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -33,8 +33,8 @@ 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: 1.5072
-- Accuracy: 0.78
+- Loss: 0.5644
+- Accuracy: 0.87
 
 ## Model description
 
@@ -59,6 +59,7 @@ The following hyperparameters were used during training:
 - seed: 42
 - 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_ratio: 0.1
 - num_epochs: 10
 - mixed_precision_training: Native AMP
 
@@ -66,16 +67,16 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 1.3311        | 1.0   | 113  | 1.2307          | 0.67     |
-| 0.7706        | 2.0   | 226  | 1.0825          | 0.63     |
-| 0.7012        | 3.0   | 339  | 0.8888          | 0.75     |
-| 0.3658        | 4.0   | 452  | 0.7326          | 0.78     |
-| 0.4113        | 5.0   | 565  | 0.9069          | 0.73     |
-| 0.0964        | 6.0   | 678  | 0.8265          | 0.82     |
-| 0.1707        | 7.0   | 791  | 0.8793          | 0.82     |
-| 0.0078        | 8.0   | 904  | 1.2400          | 0.81     |
-| 0.0136        | 9.0   | 1017 | 1.1516          | 0.79     |
-| 0.0029        | 10.0  | 1130 | 1.5072          | 0.78     |
+| 1.9682        | 1.0   | 113  | 1.9097          | 0.42     |
+| 1.2063        | 2.0   | 226  | 1.2490          | 0.7      |
+| 1.0341        | 3.0   | 339  | 0.8957          | 0.78     |
+| 0.5967        | 4.0   | 452  | 0.8012          | 0.73     |
+| 0.6163        | 5.0   | 565  | 0.6948          | 0.79     |
+| 0.2877        | 6.0   | 678  | 0.6130          | 0.82     |
+| 0.2392        | 7.0   | 791  | 0.5434          | 0.84     |
+| 0.1176        | 8.0   | 904  | 0.5847          | 0.86     |
+| 0.1138        | 9.0   | 1017 | 0.5545          | 0.86     |
+| 0.0885        | 10.0  | 1130 | 0.5644          | 0.87     |
 
 
 ### Framework versions