update model card README.md

README.md

@@ -22,7 +22,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.82
+      value: 0.87
 ---
 
 <!-- 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: 0.5914
-- Accuracy: 0.82
+- Loss: 0.6201
+- Accuracy: 0.87
 
 ## Model description
 
@@ -53,28 +53,28 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 0.0001
-- train_batch_size: 16
-- eval_batch_size: 16
+- train_batch_size: 8
+- eval_batch_size: 8
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- lr_scheduler_warmup_ratio: 0.1
+- lr_scheduler_warmup_ratio: 0.15
 - num_epochs: 10
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 1.9157        | 1.0   | 57   | 1.7318          | 0.45     |
-| 1.2425        | 2.0   | 114  | 1.1454          | 0.7      |
-| 0.8878        | 3.0   | 171  | 0.8611          | 0.76     |
-| 0.6978        | 4.0   | 228  | 0.7691          | 0.77     |
-| 0.4136        | 5.0   | 285  | 0.6523          | 0.82     |
-| 0.2548        | 6.0   | 342  | 0.5780          | 0.84     |
-| 0.183         | 7.0   | 399  | 0.6210          | 0.81     |
-| 0.0801        | 8.0   | 456  | 0.6295          | 0.82     |
-| 0.0709        | 9.0   | 513  | 0.6664          | 0.82     |
-| 0.0423        | 10.0  | 570  | 0.5914          | 0.82     |
+| 1.8953        | 1.0   | 113  | 1.7589          | 0.5      |
+| 1.223         | 2.0   | 226  | 1.0978          | 0.62     |
+| 0.8826        | 3.0   | 339  | 0.8046          | 0.75     |
+| 0.8539        | 4.0   | 452  | 0.6580          | 0.78     |
+| 0.319         | 5.0   | 565  | 0.5853          | 0.81     |
+| 0.2293        | 6.0   | 678  | 0.6173          | 0.82     |
+| 0.3119        | 7.0   | 791  | 0.5053          | 0.85     |
+| 0.0233        | 8.0   | 904  | 0.6036          | 0.86     |
+| 0.0205        | 9.0   | 1017 | 0.6029          | 0.87     |
+| 0.0091        | 10.0  | 1130 | 0.6201          | 0.87     |
 
 
 ### Framework versions