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semaj83
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distilhubert-finetuned-gtzan

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

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  1. README.md +23 -13
  2. pytorch_model.bin +1 -1
README.md CHANGED
@@ -32,7 +32,7 @@ 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: 0.6087
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  - Accuracy: 0.85
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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: 5e-05
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  - train_batch_size: 16
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  - eval_batch_size: 16
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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: 10
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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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- | 2.1053 | 1.0 | 57 | 1.9945 | 0.46 |
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- | 1.5695 | 2.0 | 114 | 1.5205 | 0.64 |
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- | 1.1237 | 3.0 | 171 | 1.1046 | 0.73 |
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- | 1.071 | 4.0 | 228 | 0.9450 | 0.73 |
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- | 0.8 | 5.0 | 285 | 0.7971 | 0.77 |
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- | 0.7019 | 6.0 | 342 | 0.7286 | 0.79 |
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- | 0.5724 | 7.0 | 399 | 0.6902 | 0.79 |
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- | 0.5266 | 8.0 | 456 | 0.6528 | 0.84 |
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- | 0.4601 | 9.0 | 513 | 0.6593 | 0.82 |
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- | 0.4203 | 10.0 | 570 | 0.6087 | 0.85 |
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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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: 0.6232
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  - Accuracy: 0.85
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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: 2e-05
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  - train_batch_size: 16
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  - eval_batch_size: 16
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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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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 2.2706 | 1.0 | 57 | 2.2603 | 0.25 |
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+ | 2.0729 | 2.0 | 114 | 2.0172 | 0.38 |
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+ | 1.7159 | 3.0 | 171 | 1.6766 | 0.62 |
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+ | 1.5972 | 4.0 | 228 | 1.4569 | 0.65 |
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+ | 1.2733 | 5.0 | 285 | 1.2656 | 0.71 |
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+ | 1.2068 | 6.0 | 342 | 1.1271 | 0.77 |
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+ | 1.1018 | 7.0 | 399 | 1.0385 | 0.77 |
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+ | 1.0269 | 8.0 | 456 | 0.9562 | 0.76 |
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+ | 0.9285 | 9.0 | 513 | 0.9170 | 0.73 |
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+ | 0.8465 | 10.0 | 570 | 0.8357 | 0.8 |
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+ | 0.6841 | 11.0 | 627 | 0.8469 | 0.78 |
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+ | 0.6767 | 12.0 | 684 | 0.7362 | 0.84 |
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+ | 0.6276 | 13.0 | 741 | 0.7254 | 0.83 |
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+ | 0.5224 | 14.0 | 798 | 0.7046 | 0.82 |
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+ | 0.5469 | 15.0 | 855 | 0.6684 | 0.85 |
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+ | 0.5789 | 16.0 | 912 | 0.6343 | 0.85 |
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+ | 0.4983 | 17.0 | 969 | 0.6367 | 0.83 |
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+ | 0.4746 | 18.0 | 1026 | 0.6301 | 0.83 |
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+ | 0.4469 | 19.0 | 1083 | 0.6157 | 0.85 |
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+ | 0.4484 | 20.0 | 1140 | 0.6232 | 0.85 |
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  ### Framework versions
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