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NiloofarMomeni
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distilhubert-finetuned-VD

Audio Classification
Transformers
Safetensors
hubert
Generated from Trainer
Eval Results
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End of training

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  1. README.md +31 -26
  2. model.safetensors +1 -1
README.md CHANGED
@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.8349877949552482
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,8 +32,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.4702
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- - Accuracy: 0.8350
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  ## Model description
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@@ -53,39 +53,44 @@ More information needed
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  The following hyperparameters were used during training:
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  - learning_rate: 5e-05
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- - train_batch_size: 8
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- - eval_batch_size: 8
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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- - num_epochs: 20
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  - mixed_precision_training: Native AMP
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 0.555 | 1.0 | 167 | 0.4702 | 0.8350 |
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- | 0.3965 | 2.0 | 334 | 0.4398 | 0.7570 |
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- | 0.4106 | 3.0 | 501 | 0.7742 | 0.6713 |
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- | 0.4372 | 4.0 | 668 | 0.9340 | 0.6827 |
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- | 0.2087 | 5.0 | 835 | 1.0133 | 0.7574 |
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- | 0.124 | 6.0 | 1002 | 1.1049 | 0.7437 |
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- | 0.0509 | 7.0 | 1169 | 1.2264 | 0.7590 |
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- | 0.0016 | 8.0 | 1336 | 1.2315 | 0.7845 |
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- | 0.0064 | 9.0 | 1503 | 1.3620 | 0.7762 |
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- | 0.0006 | 10.0 | 1670 | 1.3149 | 0.8039 |
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- | 0.0007 | 11.0 | 1837 | 1.2818 | 0.8116 |
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- | 0.0003 | 12.0 | 2004 | 1.2635 | 0.8298 |
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- | 0.0003 | 13.0 | 2171 | 1.3287 | 0.8225 |
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- | 0.0002 | 14.0 | 2338 | 1.3200 | 0.8295 |
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- | 0.0001 | 15.0 | 2505 | 1.4146 | 0.8226 |
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- | 0.0001 | 16.0 | 2672 | 1.4359 | 0.8221 |
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- | 0.0001 | 17.0 | 2839 | 1.4443 | 0.8233 |
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- | 0.0001 | 18.0 | 3006 | 1.5031 | 0.8184 |
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- | 0.0001 | 19.0 | 3173 | 1.5111 | 0.8182 |
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- | 0.0001 | 20.0 | 3340 | 1.5145 | 0.8182 |
 
 
 
 
 
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  ### Framework versions
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.8933256172839507
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.7226
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+ - Accuracy: 0.8933
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 5e-05
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+ - train_batch_size: 10
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+ - eval_batch_size: 10
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 25
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  - mixed_precision_training: Native AMP
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 0.3302 | 1.0 | 195 | 0.3716 | 0.8800 |
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+ | 0.6059 | 2.0 | 390 | 0.5195 | 0.8090 |
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+ | 0.4938 | 3.0 | 585 | 1.0102 | 0.6260 |
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+ | 0.836 | 4.0 | 780 | 1.1662 | 0.6742 |
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+ | 0.2234 | 5.0 | 975 | 0.6792 | 0.8389 |
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+ | 0.1444 | 6.0 | 1170 | 0.9137 | 0.8239 |
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+ | 0.2986 | 7.0 | 1365 | 0.7987 | 0.8623 |
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+ | 0.0004 | 8.0 | 1560 | 1.5075 | 0.7687 |
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+ | 0.0005 | 9.0 | 1755 | 0.7226 | 0.8933 |
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+ | 0.0002 | 10.0 | 1950 | 0.8246 | 0.8829 |
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+ | 0.0002 | 11.0 | 2145 | 1.4227 | 0.8129 |
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+ | 0.0001 | 12.0 | 2340 | 1.0478 | 0.8665 |
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+ | 0.0001 | 13.0 | 2535 | 1.3328 | 0.8322 |
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+ | 0.0001 | 14.0 | 2730 | 1.3480 | 0.8347 |
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+ | 0.0001 | 15.0 | 2925 | 1.3559 | 0.8370 |
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+ | 0.0 | 16.0 | 3120 | 1.3589 | 0.8407 |
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+ | 0.0 | 17.0 | 3315 | 1.3706 | 0.8410 |
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+ | 0.0 | 18.0 | 3510 | 1.3831 | 0.8410 |
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+ | 0.0 | 19.0 | 3705 | 1.3954 | 0.8410 |
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+ | 0.0 | 20.0 | 3900 | 1.4027 | 0.8412 |
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+ | 0.0 | 21.0 | 4095 | 1.4132 | 0.8409 |
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+ | 0.0 | 22.0 | 4290 | 1.4218 | 0.8407 |
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+ | 0.0 | 23.0 | 4485 | 1.4272 | 0.8407 |
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+ | 0.0 | 24.0 | 4680 | 1.4321 | 0.8399 |
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+ | 0.0 | 25.0 | 4875 | 1.4337 | 0.8399 |
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  ### Framework versions
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