update model card README.md

README.md

@@ -1,6 +1,5 @@
 ---
 license: apache-2.0
-base_model: ntu-spml/distilhubert
 tags:
 - generated_from_trainer
 datasets:
@@ -8,32 +7,19 @@ datasets:
 metrics:
 - accuracy
 model-index:
-- name: distilhubert-finetuned-gtzan3
-  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
+- name: distilhubert-finetuned-gtzan
+  results: []
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
-# distilhubert-finetuned-gtzan3
+# distilhubert-finetuned-gtzan
 
 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: 1.0442
-- Accuracy: 0.85
+- Loss: 0.6231
+- Accuracy: 0.83
 
 ## Model description
 
@@ -53,43 +39,33 @@ 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: 20
+- num_epochs: 10
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 1.9942        | 1.0   | 225  | 1.8990          | 0.51     |
-| 1.2485        | 2.0   | 450  | 1.2682          | 0.62     |
-| 0.9196        | 3.0   | 675  | 1.0459          | 0.69     |
-| 0.9034        | 4.0   | 900  | 0.8488          | 0.75     |
-| 0.3035        | 5.0   | 1125 | 0.7319          | 0.76     |
-| 0.0715        | 6.0   | 1350 | 0.8713          | 0.77     |
-| 0.1338        | 7.0   | 1575 | 0.8239          | 0.82     |
-| 0.0254        | 8.0   | 1800 | 0.9324          | 0.83     |
-| 0.0044        | 9.0   | 2025 | 0.7641          | 0.85     |
-| 0.0024        | 10.0  | 2250 | 0.9133          | 0.83     |
-| 0.19          | 11.0  | 2475 | 0.9976          | 0.84     |
-| 0.0013        | 12.0  | 2700 | 0.9684          | 0.83     |
-| 0.0011        | 13.0  | 2925 | 0.9241          | 0.85     |
-| 0.001         | 14.0  | 3150 | 0.9540          | 0.86     |
-| 0.0008        | 15.0  | 3375 | 1.0849          | 0.85     |
-| 0.0007        | 16.0  | 3600 | 0.9005          | 0.85     |
-| 0.0007        | 17.0  | 3825 | 0.9798          | 0.84     |
-| 0.0007        | 18.0  | 4050 | 1.0058          | 0.84     |
-| 0.0005        | 19.0  | 4275 | 1.0524          | 0.85     |
-| 0.0006        | 20.0  | 4500 | 1.0442          | 0.85     |
+| 1.7262        | 1.0   | 113  | 1.8018          | 0.41     |
+| 1.1879        | 2.0   | 226  | 1.2414          | 0.64     |
+| 1.0662        | 3.0   | 339  | 0.9183          | 0.76     |
+| 0.6538        | 4.0   | 452  | 0.6994          | 0.83     |
+| 0.4435        | 5.0   | 565  | 0.6452          | 0.82     |
+| 0.2902        | 6.0   | 678  | 0.5580          | 0.85     |
+| 0.1912        | 7.0   | 791  | 0.6249          | 0.82     |
+| 0.2514        | 8.0   | 904  | 0.6166          | 0.83     |
+| 0.1347        | 9.0   | 1017 | 0.6010          | 0.84     |
+| 0.2313        | 10.0  | 1130 | 0.6231          | 0.83     |
 
 
 ### Framework versions
 
-- Transformers 4.31.0
+- Transformers 4.30.2
 - Pytorch 2.0.1+cu117
 - Datasets 2.13.1
 - Tokenizers 0.13.3