update model card README.md

README.md

@@ -5,9 +5,24 @@ tags:
 - generated_from_trainer
 datasets:
 - marsyas/gtzan
+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: 0.85
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -17,13 +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:
-- eval_loss: 0.7834
-- eval_accuracy: 0.78
-- eval_runtime: 46.4733
-- eval_samples_per_second: 2.152
-- eval_steps_per_second: 0.538
-- epoch: 12.0
-- step: 1350
+- Loss: 0.6064
+- Accuracy: 0.85
 
 ## Model description
 
@@ -51,11 +61,22 @@ 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
-- num_epochs: 15
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 0.9939        | 1.0   | 112  | 0.7660          | 0.77     |
+| 0.777         | 2.0   | 225  | 0.6270          | 0.81     |
+| 0.6878        | 3.0   | 337  | 0.5810          | 0.82     |
+| 0.5696        | 4.0   | 450  | 0.5826          | 0.85     |
+| 0.4433        | 4.98  | 560  | 0.6064          | 0.85     |
+
 
 ### Framework versions
 
 - Transformers 4.32.0.dev0
-- Pytorch 2.0.1+cu117
+- Pytorch 2.0.1+cu118
 - Datasets 2.13.1
 - Tokenizers 0.13.3