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NiloofarMomeni
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distilhubert-finetuned-gtzan

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

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  1. README.md +26 -8
  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: 0.8458372902817347
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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: 0.4257
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- - Accuracy: 0.8458
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  ## Model description
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@@ -59,15 +59,33 @@ The following hyperparameters were used during training:
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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: 2
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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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- | 0.3864 | 1.0 | 7108 | 0.4585 | 0.8050 |
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- | 0.3188 | 2.0 | 14216 | 0.4257 | 0.8458 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: 0.8581829692940804
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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: 1.5627
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+ - Accuracy: 0.8582
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  ## Model description
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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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+ | 0.4173 | 1.0 | 7108 | 0.5416 | 0.8343 |
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+ | 0.235 | 2.0 | 14216 | 0.4663 | 0.8251 |
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+ | 0.1549 | 3.0 | 21324 | 0.5940 | 0.8325 |
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+ | 0.2558 | 4.0 | 28432 | 0.6608 | 0.8531 |
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+ | 0.2991 | 5.0 | 35540 | 0.9088 | 0.8305 |
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+ | 0.4773 | 6.0 | 42648 | 0.9120 | 0.8390 |
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+ | 0.5235 | 7.0 | 49756 | 0.9285 | 0.8455 |
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+ | 0.0004 | 8.0 | 56864 | 1.0259 | 0.8492 |
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+ | 0.1918 | 9.0 | 63972 | 1.2874 | 0.8411 |
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+ | 0.0002 | 10.0 | 71080 | 1.1114 | 0.8476 |
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+ | 0.0001 | 11.0 | 78188 | 1.4835 | 0.8393 |
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+ | 0.0013 | 12.0 | 85296 | 1.3846 | 0.8541 |
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+ | 0.0001 | 13.0 | 92404 | 1.3622 | 0.8507 |
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+ | 0.0909 | 14.0 | 99512 | 1.4672 | 0.8487 |
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+ | 0.0001 | 15.0 | 106620 | 1.4243 | 0.8571 |
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+ | 0.0 | 16.0 | 113728 | 1.5627 | 0.8582 |
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+ | 0.0 | 17.0 | 120836 | 1.8146 | 0.8531 |
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+ | 0.0 | 18.0 | 127944 | 1.8596 | 0.8550 |
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+ | 0.0 | 19.0 | 135052 | 1.9233 | 0.8574 |
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+ | 0.0 | 20.0 | 142160 | 1.9875 | 0.8569 |
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  ### Framework versions
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