update model card README.md

README.md

@@ -4,24 +4,9 @@ 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
@@ -31,8 +16,13 @@ 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: 0.5140
-- Accuracy: 0.85
+- eval_loss: 0.5092
+- eval_accuracy: 0.8824
+- eval_runtime: 17.8602
+- eval_samples_per_second: 1.904
+- eval_steps_per_second: 0.504
+- epoch: 2.0
+- step: 150
 
 ## Model description
 
@@ -52,29 +42,13 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 5e-05
-- train_batch_size: 8
-- eval_batch_size: 8
+- train_batch_size: 4
+- eval_batch_size: 4
 - 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
-
-### Training results
-
-| Training Loss | Epoch | Step | Validation Loss | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 2.0082        | 1.0   | 113  | 1.8364          | 0.42     |
-| 1.3116        | 2.0   | 226  | 1.2265          | 0.67     |
-| 1.0207        | 3.0   | 339  | 0.9318          | 0.73     |
-| 0.9157        | 4.0   | 452  | 0.8398          | 0.74     |
-| 0.6641        | 5.0   | 565  | 0.6821          | 0.8      |
-| 0.3651        | 6.0   | 678  | 0.5933          | 0.82     |
-| 0.4257        | 7.0   | 791  | 0.5077          | 0.86     |
-| 0.1812        | 8.0   | 904  | 0.5231          | 0.86     |
-| 0.2592        | 9.0   | 1017 | 0.4903          | 0.84     |
-| 0.1195        | 10.0  | 1130 | 0.5140          | 0.85     |
-
+- num_epochs: 13
 
 ### Framework versions