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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-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: None |
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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: 0.9 |
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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-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: 0.3410 |
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- Accuracy: 0.9 |
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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: 3e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 25 |
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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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| 1.627 | 1.0 | 50 | 0.8714 | 0.795 | |
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| 0.4145 | 2.0 | 100 | 0.5660 | 0.825 | |
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| 0.2344 | 3.0 | 150 | 0.4988 | 0.85 | |
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| 0.1334 | 4.0 | 200 | 0.3726 | 0.87 | |
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| 0.0341 | 5.0 | 250 | 0.3637 | 0.895 | |
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| 0.0172 | 6.0 | 300 | 0.4197 | 0.87 | |
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| 0.0338 | 7.0 | 350 | 0.5035 | 0.87 | |
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| 0.002 | 8.0 | 400 | 0.5825 | 0.86 | |
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| 0.001 | 9.0 | 450 | 0.4126 | 0.895 | |
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| 0.0093 | 10.0 | 500 | 0.4564 | 0.89 | |
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| 0.0056 | 11.0 | 550 | 0.4783 | 0.84 | |
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| 0.0162 | 12.0 | 600 | 0.3161 | 0.89 | |
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| 0.0019 | 13.0 | 650 | 0.4062 | 0.875 | |
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| 0.0005 | 14.0 | 700 | 0.3630 | 0.895 | |
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| 0.0098 | 15.0 | 750 | 0.3410 | 0.9 | |
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| 0.008 | 16.0 | 800 | 0.3385 | 0.89 | |
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| 0.0001 | 17.0 | 850 | 0.3434 | 0.895 | |
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| 0.0067 | 18.0 | 900 | 0.3414 | 0.885 | |
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| 0.0064 | 19.0 | 950 | 0.3453 | 0.895 | |
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| 0.0001 | 20.0 | 1000 | 0.3422 | 0.885 | |
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| 0.0001 | 21.0 | 1050 | 0.3520 | 0.89 | |
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| 0.0036 | 22.0 | 1100 | 0.3403 | 0.89 | |
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| 0.0001 | 23.0 | 1150 | 0.3394 | 0.89 | |
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| 0.0001 | 24.0 | 1200 | 0.3407 | 0.89 | |
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| 0.0026 | 25.0 | 1250 | 0.3417 | 0.89 | |
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### Framework versions |
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- Transformers 4.57.3 |
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- Pytorch 2.9.0+cu128 |
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- Datasets 2.18.0 |
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- Tokenizers 0.22.1 |
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