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--- |
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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-2 |
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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-2 |
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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: 0.7203 |
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- Accuracy: 0.86 |
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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: 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: 0.2 |
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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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| 2.2521 | 1.0 | 90 | 2.2219 | 0.3 | |
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| 1.8502 | 2.0 | 180 | 1.8299 | 0.54 | |
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| 1.4155 | 3.0 | 270 | 1.4247 | 0.64 | |
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| 0.9885 | 4.0 | 360 | 1.0313 | 0.7 | |
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| 0.8111 | 5.0 | 450 | 0.8535 | 0.78 | |
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| 0.7023 | 6.0 | 540 | 0.7743 | 0.79 | |
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| 0.5663 | 7.0 | 630 | 0.6618 | 0.81 | |
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| 0.3577 | 8.0 | 720 | 0.6937 | 0.77 | |
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| 0.3003 | 9.0 | 810 | 0.6107 | 0.82 | |
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| 0.1321 | 10.0 | 900 | 0.5648 | 0.81 | |
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| 0.0488 | 11.0 | 990 | 0.5655 | 0.84 | |
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| 0.0323 | 12.0 | 1080 | 0.5612 | 0.86 | |
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| 0.0154 | 13.0 | 1170 | 0.6338 | 0.85 | |
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| 0.0108 | 14.0 | 1260 | 0.7292 | 0.84 | |
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| 0.0082 | 15.0 | 1350 | 0.7542 | 0.84 | |
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| 0.0065 | 16.0 | 1440 | 0.7123 | 0.86 | |
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| 0.0062 | 17.0 | 1530 | 0.6949 | 0.86 | |
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| 0.0848 | 18.0 | 1620 | 0.7332 | 0.85 | |
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| 0.0053 | 19.0 | 1710 | 0.7291 | 0.85 | |
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| 0.005 | 20.0 | 1800 | 0.7203 | 0.86 | |
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### Framework versions |
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- Transformers 4.38.1 |
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- Pytorch 2.2.1 |
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- Datasets 2.17.1 |
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- Tokenizers 0.15.2 |
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