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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: ntu-spml/distilhubert |
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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: distilhubert-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: 0.86 |
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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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# distilhubert-finetuned-gtzan |
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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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- Accuracy: 0.88 |
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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: 8e-05 |
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- train_batch_size: 12 |
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- eval_batch_size: 12 |
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- seed: 42 |
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- optimizer: Use 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: 20 |
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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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| 2.1317 | 1.0 | 75 | 2.0386 | 0.33 | |
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| 1.36 | 2.0 | 150 | 1.4142 | 0.58 | |
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| 1.1456 | 3.0 | 225 | 1.1110 | 0.66 | |
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| 0.6417 | 4.0 | 300 | 1.0142 | 0.69 | |
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| 0.3324 | 5.0 | 375 | 0.5881 | 0.82 | |
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| 0.2208 | 6.0 | 450 | 0.5516 | 0.84 | |
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| 0.3346 | 7.0 | 525 | 0.5267 | 0.87 | |
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| 0.2309 | 8.0 | 600 | 0.7404 | 0.8 | |
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| 0.0267 | 9.0 | 675 | 0.6636 | 0.8 | |
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| 0.0309 | 10.0 | 750 | 0.6390 | 0.84 | |
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| 0.0076 | 11.0 | 825 | 0.6949 | 0.85 | |
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| 0.0053 | 12.0 | 900 | 0.6405 | 0.87 | |
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| 0.005 | 13.0 | 975 | 0.7065 | 0.84 | |
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| 0.004 | 14.0 | 1050 | 0.8570 | 0.84 | |
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| 0.0031 | 15.0 | 1125 | 0.6735 | 0.88 | |
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| 0.0028 | 16.0 | 1200 | 0.7023 | 0.85 | |
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| 0.0027 | 17.0 | 1275 | 0.6823 | 0.86 | |
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| 0.0369 | 18.0 | 1350 | 0.7320 | 0.85 | |
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| 0.0024 | 19.0 | 1425 | 0.6656 | 0.86 | |
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| 0.0023 | 20.0 | 1500 | 0.6628 | 0.86 | |
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### Framework versions |
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- Transformers 4.46.2 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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