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vapari
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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 +21 -15
  2. model.safetensors +1 -1
README.md CHANGED
@@ -23,7 +23,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.7435897435897436
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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
@@ -33,8 +33,8 @@ 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: 0.9861
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- - Accuracy: 0.7436
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  ## Model description
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@@ -53,10 +53,12 @@ More information needed
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  ### Training hyperparameters
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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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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 2.1171 | 1.0 | 70 | 2.1232 | 0.2308 |
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- | 1.534 | 2.0 | 140 | 1.6014 | 0.5128 |
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- | 1.4328 | 3.0 | 210 | 1.2896 | 0.5641 |
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- | 0.8631 | 4.0 | 280 | 1.1275 | 0.5897 |
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- | 0.6448 | 5.0 | 350 | 1.0679 | 0.6667 |
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- | 0.482 | 6.0 | 420 | 0.8798 | 0.7051 |
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- | 0.2458 | 7.0 | 490 | 0.8290 | 0.7564 |
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- | 0.2264 | 8.0 | 560 | 0.8350 | 0.7564 |
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- | 0.1661 | 9.0 | 630 | 0.8284 | 0.7179 |
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- | 0.0286 | 10.0 | 700 | 0.9681 | 0.7179 |
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- | 0.0155 | 11.0 | 770 | 0.9861 | 0.7436 |
 
 
 
 
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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: 0.717948717948718
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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: 0.8872
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+ - Accuracy: 0.7179
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 4e-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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+ - gradient_accumulation_steps: 2
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+ - total_train_batch_size: 20
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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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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 2.2117 | 1.0 | 35 | 2.1969 | 0.1923 |
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+ | 1.9698 | 2.0 | 70 | 1.9327 | 0.3846 |
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+ | 1.6629 | 3.0 | 105 | 1.5580 | 0.5 |
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+ | 1.2324 | 4.0 | 140 | 1.3368 | 0.6154 |
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+ | 1.0466 | 5.0 | 175 | 1.1638 | 0.6538 |
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+ | 0.8969 | 6.0 | 210 | 1.0416 | 0.6923 |
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+ | 0.7626 | 7.0 | 245 | 0.9258 | 0.7436 |
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+ | 0.6015 | 8.0 | 280 | 1.0475 | 0.6667 |
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+ | 0.5003 | 9.0 | 315 | 0.8890 | 0.7308 |
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+ | 0.3956 | 10.0 | 350 | 0.8396 | 0.7564 |
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+ | 0.3228 | 11.0 | 385 | 0.8072 | 0.6795 |
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+ | 0.2558 | 12.0 | 420 | 0.7788 | 0.7308 |
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+ | 0.1901 | 13.0 | 455 | 0.8432 | 0.7308 |
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+ | 0.1251 | 14.0 | 490 | 0.8287 | 0.7051 |
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+ | 0.1185 | 15.0 | 525 | 0.8872 | 0.7179 |
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  ### Framework versions
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