update model card README.md

README.md

@@ -22,7 +22,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.86
+      value: 0.85
 ---
 
 <!-- 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: 0.5269
-- Accuracy: 0.86
+- Loss: 1.1378
+- Accuracy: 0.85
 
 ## Model description
 
@@ -56,8 +56,6 @@ The following hyperparameters were used during training:
 - train_batch_size: 8
 - eval_batch_size: 8
 - seed: 42
-- gradient_accumulation_steps: 2
-- total_train_batch_size: 16
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_ratio: 0.1
@@ -67,16 +65,16 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 1.9286        | 0.99  | 56   | 1.7845          | 0.56     |
-| 1.2162        | 2.0   | 113  | 1.1310          | 0.74     |
-| 0.8715        | 2.99  | 169  | 0.8334          | 0.75     |
-| 0.6735        | 4.0   | 226  | 0.7352          | 0.79     |
-| 0.4007        | 4.99  | 282  | 0.5135          | 0.87     |
-| 0.241         | 6.0   | 339  | 0.7801          | 0.76     |
-| 0.181         | 6.99  | 395  | 0.5440          | 0.81     |
-| 0.1336        | 8.0   | 452  | 0.5280          | 0.85     |
-| 0.0526        | 8.99  | 508  | 0.4992          | 0.87     |
-| 0.0315        | 9.91  | 560  | 0.5269          | 0.86     |
+| 0.2304        | 1.0   | 113  | 1.3338          | 0.74     |
+| 0.2152        | 2.0   | 226  | 1.6611          | 0.72     |
+| 0.1082        | 3.0   | 339  | 1.6029          | 0.75     |
+| 0.0991        | 4.0   | 452  | 1.3034          | 0.8      |
+| 0.1046        | 5.0   | 565  | 1.1716          | 0.82     |
+| 0.0003        | 6.0   | 678  | 1.1419          | 0.85     |
+| 0.0004        | 7.0   | 791  | 1.2336          | 0.81     |
+| 0.0009        | 8.0   | 904  | 1.0938          | 0.85     |
+| 0.0002        | 9.0   | 1017 | 1.1084          | 0.85     |
+| 0.0002        | 10.0  | 1130 | 1.1378          | 0.85     |
 
 
 ### Framework versions