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Padajno
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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 +19 -15
  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: 0.8
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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: 0.8209
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- - Accuracy: 0.8
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  ## Model description
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@@ -59,23 +59,27 @@ 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: 10
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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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- | 1.8561 | 1.0 | 225 | 1.6555 | 0.56 |
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- | 1.109 | 2.0 | 450 | 1.2396 | 0.58 |
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- | 0.6901 | 3.0 | 675 | 0.8904 | 0.71 |
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- | 0.2618 | 4.0 | 900 | 0.6728 | 0.8 |
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- | 0.296 | 5.0 | 1125 | 0.6022 | 0.8 |
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- | 0.1734 | 6.0 | 1350 | 0.6310 | 0.83 |
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- | 0.1562 | 7.0 | 1575 | 0.6711 | 0.8 |
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- | 0.1927 | 8.0 | 1800 | 0.7798 | 0.8 |
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- | 0.0102 | 9.0 | 2025 | 0.8040 | 0.78 |
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- | 0.0102 | 10.0 | 2250 | 0.8209 | 0.8 |
 
 
 
 
 
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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: 0.82
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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: 1.0676
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+ - Accuracy: 0.82
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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: 15
 
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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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+ | 0.0002 | 1.0 | 225 | 2.0510 | 0.78 |
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+ | 0.67 | 2.0 | 450 | 2.3754 | 0.77 |
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+ | 0.0002 | 3.0 | 675 | 1.2463 | 0.83 |
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+ | 0.0 | 4.0 | 900 | 1.4864 | 0.82 |
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+ | 0.0001 | 5.0 | 1125 | 1.6275 | 0.8 |
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+ | 0.0 | 6.0 | 1350 | 1.4957 | 0.84 |
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+ | 0.0003 | 7.0 | 1575 | 1.4223 | 0.83 |
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+ | 0.0001 | 8.0 | 1800 | 0.9586 | 0.89 |
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+ | 0.0001 | 9.0 | 2025 | 1.4912 | 0.83 |
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+ | 0.0001 | 10.0 | 2250 | 1.3005 | 0.83 |
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+ | 0.0 | 11.0 | 2475 | 1.0646 | 0.83 |
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+ | 0.0 | 12.0 | 2700 | 1.0408 | 0.84 |
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+ | 0.0 | 13.0 | 2925 | 1.0233 | 0.84 |
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+ | 0.0 | 14.0 | 3150 | 1.0709 | 0.83 |
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+ | 0.0 | 15.0 | 3375 | 1.0676 | 0.82 |
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  ### Framework versions
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