End of training

README.md

@@ -5,24 +5,9 @@ tags:
 - generated_from_trainer
 datasets:
 - marsyas/gtzan
-metrics:
-- accuracy
 model-index:
 - name: distilhubert-finetuned-gtzan
-  results:
-  - task:
-      name: Audio Classification
-      type: audio-classification
-    dataset:
-      name: GTZAN
-      type: marsyas/gtzan
-      config: all
-      split: train
-      args: all
-    metrics:
-    - name: Accuracy
-      type: accuracy
-      value: 0.85
+  results: []
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,8 +17,7 @@ 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: 0.6638
-- Accuracy: 0.85
+- Loss: 0.4454
 
 ## Model description
 
@@ -52,29 +36,28 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 5e-05
-- train_batch_size: 8
+- learning_rate: 9.349509030319398e-05
+- train_batch_size: 12
 - 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
-- num_epochs: 10
+- num_epochs: 9
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 2.0133        | 1.0   | 113  | 1.8013          | 0.49     |
-| 1.3855        | 2.0   | 226  | 1.2793          | 0.68     |
-| 0.9643        | 3.0   | 339  | 0.9452          | 0.74     |
-| 0.8143        | 4.0   | 452  | 0.8508          | 0.73     |
-| 0.613         | 5.0   | 565  | 0.7258          | 0.75     |
-| 0.3513        | 6.0   | 678  | 0.7811          | 0.76     |
-| 0.409         | 7.0   | 791  | 0.6630          | 0.81     |
-| 0.1786        | 8.0   | 904  | 0.6641          | 0.84     |
-| 0.2988        | 9.0   | 1017 | 0.6572          | 0.83     |
-| 0.1114        | 10.0  | 1130 | 0.6638          | 0.85     |
+| Training Loss | Epoch | Step | Validation Loss |
+|:-------------:|:-----:|:----:|:---------------:|
+| 1.904         | 1.0   | 75   | 1.7595          |
+| 1.2214        | 2.0   | 150  | 1.1147          |
+| 0.862         | 3.0   | 225  | 0.7765          |
+| 0.6679        | 4.0   | 300  | 0.6600          |
+| 0.4188        | 5.0   | 375  | 0.4797          |
+| 0.3369        | 6.0   | 450  | 0.5607          |
+| 0.1591        | 7.0   | 525  | 0.4668          |
+| 0.0591        | 8.0   | 600  | 0.4493          |
+| 0.0718        | 9.0   | 675  | 0.4454          |
 
 
 ### Framework versions