End of training

README.md

@@ -22,7 +22,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.85
+      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: 0.7283
-- Accuracy: 0.85
+- Loss: 0.5522
+- Accuracy: 0.87
 
 ## Model description
 
@@ -53,30 +53,26 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 0.0001
-- train_batch_size: 4
-- eval_batch_size: 4
+- train_batch_size: 8
+- eval_batch_size: 8
 - 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: 10
+- num_epochs: 8
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 1.8021        | 1.0   | 112  | 1.6488          | 0.43     |
-| 1.0861        | 2.0   | 225  | 1.0086          | 0.67     |
-| 0.6939        | 3.0   | 337  | 0.7443          | 0.74     |
-| 0.5646        | 4.0   | 450  | 0.8549          | 0.74     |
-| 0.3003        | 5.0   | 562  | 0.7615          | 0.78     |
-| 0.0586        | 6.0   | 675  | 0.7139          | 0.82     |
-| 0.1372        | 7.0   | 787  | 0.7026          | 0.81     |
-| 0.0961        | 8.0   | 900  | 0.8469          | 0.83     |
-| 0.0118        | 9.0   | 1012 | 0.6534          | 0.85     |
-| 0.0742        | 9.96  | 1120 | 0.7283          | 0.85     |
+| 1.8034        | 1.0   | 113  | 1.5716          | 0.52     |
+| 1.0738        | 2.0   | 226  | 1.0565          | 0.62     |
+| 0.852         | 3.0   | 339  | 0.7845          | 0.76     |
+| 0.7287        | 4.0   | 452  | 0.7007          | 0.78     |
+| 0.4968        | 5.0   | 565  | 0.5528          | 0.82     |
+| 0.1266        | 6.0   | 678  | 0.7303          | 0.81     |
+| 0.1341        | 7.0   | 791  | 0.5915          | 0.85     |
+| 0.0251        | 8.0   | 904  | 0.5522          | 0.87     |
 
 
 ### Framework versions