End of training

README.md

@@ -22,7 +22,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.84
+      value: 0.86
 ---
 
 <!-- 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: 0.6404
-- Accuracy: 0.84
+- Loss: 0.8005
+- Accuracy: 0.86
 
 ## Model description
 
@@ -59,22 +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
-- training_steps: 1000
+- training_steps: 2000
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 2.0825        | 0.88  | 100  | 1.8392          | 0.47     |
-| 1.4043        | 1.77  | 200  | 1.2675          | 0.67     |
-| 1.0686        | 2.65  | 300  | 1.0186          | 0.71     |
-| 0.8037        | 3.54  | 400  | 0.9198          | 0.74     |
-| 0.6215        | 4.42  | 500  | 0.7636          | 0.78     |
-| 0.5106        | 5.31  | 600  | 0.7937          | 0.76     |
-| 0.3844        | 6.19  | 700  | 0.6909          | 0.78     |
-| 0.3043        | 7.08  | 800  | 0.7279          | 0.77     |
-| 0.2453        | 7.96  | 900  | 0.6447          | 0.82     |
-| 0.211         | 8.85  | 1000 | 0.6404          | 0.84     |
+| Training Loss | Epoch | Step | Accuracy | Validation Loss |
+|:-------------:|:-----:|:----:|:--------:|:---------------:|
+| 2.0825        | 0.88  | 100  | 0.47     | 1.8392          |
+| 1.4043        | 1.77  | 200  | 0.67     | 1.2675          |
+| 1.0686        | 2.65  | 300  | 0.71     | 1.0186          |
+| 0.8037        | 3.54  | 400  | 0.74     | 0.9198          |
+| 0.6215        | 4.42  | 500  | 0.78     | 0.7636          |
+| 0.5106        | 5.31  | 600  | 0.76     | 0.7937          |
+| 0.3844        | 6.19  | 700  | 0.78     | 0.6909          |
+| 0.3043        | 7.08  | 800  | 0.77     | 0.7279          |
+| 0.2453        | 7.96  | 900  | 0.82     | 0.6447          |
+| 0.211         | 8.85  | 1000 | 0.84     | 0.6404          |
+| 0.2268        | 9.73  | 1100 | 0.7198   | 0.77            |
+| 0.1565        | 10.62 | 1200 | 0.6704   | 0.83            |
+| 0.0694        | 11.5  | 1300 | 0.8017   | 0.83            |
+| 0.0568        | 12.39 | 1400 | 0.7841   | 0.8             |
+| 0.0441        | 13.27 | 1500 | 0.7757   | 0.81            |
+| 0.0302        | 14.16 | 1600 | 0.7819   | 0.84            |
+| 0.0116        | 15.04 | 1700 | 0.7949   | 0.83            |
+| 0.0289        | 15.93 | 1800 | 0.8057   | 0.85            |
+| 0.0115        | 16.81 | 1900 | 0.8271   | 0.83            |
+| 0.0081        | 17.7  | 2000 | 0.8005   | 0.86            |
 
 
 ### Framework versions