update model card README.md

README.md

@@ -22,7 +22,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.49
+      value: 0.84
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,8 +32,8 @@ 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: 1.8337
-- Accuracy: 0.49
+- Loss: 0.8605
+- Accuracy: 0.84
 
 ## Model description
 
@@ -59,13 +59,32 @@ 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: 1
+- num_epochs: 20
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 1.9906        | 1.0   | 113  | 1.8337          | 0.49     |
+| 2.1622        | 1.0   | 113  | 2.0289          | 0.36     |
+| 1.6015        | 2.0   | 226  | 1.4290          | 0.59     |
+| 1.1929        | 3.0   | 339  | 1.1003          | 0.7      |
+| 0.9015        | 4.0   | 452  | 0.8761          | 0.76     |
+| 0.7038        | 5.0   | 565  | 0.7516          | 0.76     |
+| 0.3261        | 6.0   | 678  | 0.7753          | 0.77     |
+| 0.5327        | 7.0   | 791  | 0.6131          | 0.79     |
+| 0.1239        | 8.0   | 904  | 0.6283          | 0.8      |
+| 0.1193        | 9.0   | 1017 | 0.5770          | 0.85     |
+| 0.1405        | 10.0  | 1130 | 0.7979          | 0.8      |
+| 0.0113        | 11.0  | 1243 | 0.7830          | 0.81     |
+| 0.1392        | 12.0  | 1356 | 0.7350          | 0.85     |
+| 0.0065        | 13.0  | 1469 | 0.7935          | 0.82     |
+| 0.0049        | 14.0  | 1582 | 0.8323          | 0.84     |
+| 0.0041        | 15.0  | 1695 | 0.7644          | 0.86     |
+| 0.0034        | 16.0  | 1808 | 0.8438          | 0.85     |
+| 0.0247        | 17.0  | 1921 | 0.7781          | 0.86     |
+| 0.003         | 18.0  | 2034 | 0.8368          | 0.84     |
+| 0.0028        | 19.0  | 2147 | 0.8477          | 0.84     |
+| 0.0031        | 20.0  | 2260 | 0.8605          | 0.84     |
 
 
 ### Framework versions