update model card README.md

README.md

@@ -1,5 +1,6 @@
 ---
 license: apache-2.0
+base_model: ntu-spml/distilhubert
 tags:
 - generated_from_trainer
 datasets:
@@ -8,7 +9,20 @@ metrics:
 - accuracy
 model-index:
 - name: distilhubert-finetuned-gtzan
-  results: []
+  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: 0.84
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -18,8 +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: 0.7339
-- Accuracy: 0.82
+- Loss: 0.7913
+- Accuracy: 0.84
 
 ## Model description
 
@@ -39,33 +53,43 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 5e-05
-- train_batch_size: 4
-- eval_batch_size: 4
+- 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
-- num_epochs: 10
+- num_epochs: 20
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 1.9233        | 1.0   | 225  | 1.7014          | 0.49     |
-| 0.8822        | 2.0   | 450  | 1.0546          | 0.68     |
-| 0.676         | 3.0   | 675  | 0.7165          | 0.78     |
-| 0.8326        | 4.0   | 900  | 0.5948          | 0.79     |
-| 0.3184        | 5.0   | 1125 | 0.5484          | 0.81     |
-| 0.6154        | 6.0   | 1350 | 0.5977          | 0.83     |
-| 0.0305        | 7.0   | 1575 | 0.6213          | 0.81     |
-| 0.0154        | 8.0   | 1800 | 0.7479          | 0.79     |
-| 0.086         | 9.0   | 2025 | 0.6926          | 0.84     |
-| 0.0103        | 10.0  | 2250 | 0.7339          | 0.82     |
+| 2.182         | 1.0   | 113  | 2.0488          | 0.51     |
+| 1.5191        | 2.0   | 226  | 1.4777          | 0.63     |
+| 1.1082        | 3.0   | 339  | 1.0471          | 0.74     |
+| 1.1174        | 4.0   | 452  | 0.9705          | 0.71     |
+| 0.5903        | 5.0   | 565  | 0.7648          | 0.78     |
+| 0.4231        | 6.0   | 678  | 0.6599          | 0.79     |
+| 0.3242        | 7.0   | 791  | 0.5716          | 0.85     |
+| 0.0799        | 8.0   | 904  | 0.7228          | 0.8      |
+| 0.2491        | 9.0   | 1017 | 0.5883          | 0.85     |
+| 0.0403        | 10.0  | 1130 | 0.7826          | 0.83     |
+| 0.0093        | 11.0  | 1243 | 0.7241          | 0.86     |
+| 0.1129        | 12.0  | 1356 | 0.6913          | 0.85     |
+| 0.0051        | 13.0  | 1469 | 0.7453          | 0.87     |
+| 0.0046        | 14.0  | 1582 | 0.7348          | 0.86     |
+| 0.0039        | 15.0  | 1695 | 0.7435          | 0.85     |
+| 0.0031        | 16.0  | 1808 | 0.7868          | 0.88     |
+| 0.0523        | 17.0  | 1921 | 0.7812          | 0.84     |
+| 0.0029        | 18.0  | 2034 | 0.7900          | 0.84     |
+| 0.0031        | 19.0  | 2147 | 0.7909          | 0.84     |
+| 0.0038        | 20.0  | 2260 | 0.7913          | 0.84     |
 
 
 ### Framework versions
 
-- Transformers 4.30.2
-- Pytorch 2.0.0
-- Datasets 2.1.0
+- Transformers 4.31.0
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.1
 - Tokenizers 0.13.3