update model card README.md

README.md

@@ -18,8 +18,8 @@ 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: 0.8681
-- Accuracy: 0.77
+- Loss: 0.9570
+- Accuracy: 0.86
 
 ## Model description
 
@@ -42,18 +42,37 @@ The following hyperparameters were used during training:
 - train_batch_size: 4
 - eval_batch_size: 4
 - seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 8
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_ratio: 0.1
-- num_epochs: 3
+- num_epochs: 20
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 1.6889        | 1.0   | 225  | 1.4743          | 0.59     |
-| 1.0306        | 2.0   | 450  | 1.0034          | 0.77     |
-| 0.7468        | 3.0   | 675  | 0.8681          | 0.77     |
+| 2.1586        | 1.0   | 112  | 2.0855          | 0.45     |
+| 1.4771        | 2.0   | 225  | 1.3396          | 0.72     |
+| 1.181         | 3.0   | 337  | 0.9735          | 0.76     |
+| 0.8133        | 4.0   | 450  | 0.8692          | 0.76     |
+| 0.5397        | 5.0   | 562  | 0.7118          | 0.81     |
+| 0.3424        | 6.0   | 675  | 0.6237          | 0.81     |
+| 0.2717        | 7.0   | 787  | 0.6551          | 0.83     |
+| 0.2653        | 8.0   | 900  | 0.6707          | 0.83     |
+| 0.0503        | 9.0   | 1012 | 0.7025          | 0.84     |
+| 0.0168        | 10.0  | 1125 | 0.7643          | 0.87     |
+| 0.1125        | 11.0  | 1237 | 0.8550          | 0.86     |
+| 0.155         | 12.0  | 1350 | 0.9796          | 0.82     |
+| 0.005         | 13.0  | 1462 | 0.9539          | 0.86     |
+| 0.0038        | 14.0  | 1575 | 0.9206          | 0.86     |
+| 0.0035        | 15.0  | 1687 | 0.8725          | 0.88     |
+| 0.051         | 16.0  | 1800 | 0.9980          | 0.86     |
+| 0.003         | 17.0  | 1912 | 0.9579          | 0.86     |
+| 0.0025        | 18.0  | 2025 | 0.9735          | 0.86     |
+| 0.0023        | 19.0  | 2137 | 0.9589          | 0.86     |
+| 0.0022        | 19.91 | 2240 | 0.9570          | 0.86     |
 
 
 ### Framework versions