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---
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library_name: transformers
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license: bsd-3-clause
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base_model: MIT/ast-finetuned-audioset-10-10-0.4593
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tags:
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- generated_from_trainer
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datasets:
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- marsyas/gtzan
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metrics:
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- accuracy
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model-index:
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- name: ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan
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results:
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- task:
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name: Audio Classification
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type: audio-classification
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dataset:
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name: GTZAN
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type: marsyas/gtzan
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config: all
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split: train
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args: all
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.91
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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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should probably proofread and complete it, then remove this comment. -->
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# ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan
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This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the GTZAN dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4517
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- Accuracy: 0.91
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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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: 2
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- eval_batch_size: 2
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 8
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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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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- 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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| 1.3017 | 1.0 | 113 | 0.6180 | 0.78 |
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| 0.5478 | 2.0 | 226 | 0.8031 | 0.77 |
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| 0.3357 | 3.0 | 339 | 0.6511 | 0.87 |
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| 0.1565 | 4.0 | 452 | 0.6858 | 0.87 |
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| 0.0628 | 5.0 | 565 | 0.5638 | 0.86 |
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| 0.0466 | 6.0 | 678 | 0.4399 | 0.91 |
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| 0.0108 | 7.0 | 791 | 0.5120 | 0.88 |
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| 0.0094 | 8.0 | 904 | 0.4854 | 0.89 |
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| 0.0069 | 9.0 | 1017 | 0.4865 | 0.91 |
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| 0.0061 | 10.0 | 1130 | 0.4674 | 0.91 |
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| 0.0052 | 11.0 | 1243 | 0.4565 | 0.91 |
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| 0.0027 | 12.0 | 1356 | 0.4557 | 0.91 |
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| 0.0042 | 13.0 | 1469 | 0.4534 | 0.91 |
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| 0.0028 | 14.0 | 1582 | 0.4523 | 0.91 |
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| 0.0026 | 14.8711 | 1680 | 0.4517 | 0.91 |
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### Framework versions
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- Transformers 4.48.2
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- Pytorch 2.6.0+cu126
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- Datasets 3.2.0
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- Tokenizers 0.21.0
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