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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: music-genre-classifer-20-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: 0.82 |
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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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# music-genre-classifer-20-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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- Loss: 0.5510 |
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- Accuracy: 0.82 |
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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: 2e-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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- 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: 30 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Accuracy | Validation Loss | |
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|:-------------:|:-----:|:----:|:--------:|:---------------:| |
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| 2.201 | 1.0 | 113 | 0.39 | 2.1256 | |
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| 1.6789 | 2.0 | 226 | 0.59 | 1.6543 | |
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| 1.5602 | 3.0 | 339 | 0.64 | 1.3917 | |
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| 1.1966 | 4.0 | 452 | 0.67 | 1.1946 | |
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| 1.1131 | 5.0 | 565 | 0.77 | 1.0492 | |
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| 1.0258 | 6.0 | 678 | 0.76 | 0.9712 | |
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| 0.988 | 7.0 | 791 | 0.76 | 0.9160 | |
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| 0.7303 | 8.0 | 904 | 0.8 | 0.8704 | |
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| 0.8036 | 9.0 | 1017 | 0.8 | 0.8425 | |
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| 0.742 | 10.0 | 1130 | 0.81 | 0.8224 | |
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| 0.7463 | 11.0 | 1243 | 0.81 | 0.8140 | |
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| 0.7428 | 12.0 | 1356 | 0.78 | 0.8112 | |
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| 0.6081 | 13.0 | 1469 | 0.82 | 0.6975 | |
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| 0.8154 | 14.0 | 1582 | 0.84 | 0.6636 | |
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| 0.3758 | 15.0 | 1695 | 0.84 | 0.6215 | |
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| 0.503 | 16.0 | 1808 | 0.81 | 0.6251 | |
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| 0.4542 | 17.0 | 1921 | 0.84 | 0.5869 | |
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| 0.3285 | 18.0 | 2034 | 0.85 | 0.5830 | |
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| 0.4309 | 19.0 | 2147 | 0.82 | 0.5844 | |
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| 0.342 | 20.0 | 2260 | 0.85 | 0.5840 | |
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| 0.3051 | 21.0 | 2373 | 0.83 | 0.5843 | |
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| 0.3558 | 22.0 | 2486 | 0.6144 | 0.79 | |
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| 0.3371 | 23.0 | 2599 | 0.5673 | 0.81 | |
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| 0.2882 | 24.0 | 2712 | 0.5365 | 0.84 | |
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| 0.2326 | 25.0 | 2825 | 0.5848 | 0.83 | |
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| 0.192 | 26.0 | 2938 | 0.5406 | 0.85 | |
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| 0.1528 | 27.0 | 3051 | 0.5482 | 0.82 | |
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| 0.1937 | 28.0 | 3164 | 0.5448 | 0.84 | |
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| 0.1264 | 29.0 | 3277 | 0.5487 | 0.84 | |
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| 0.1356 | 30.0 | 3390 | 0.5510 | 0.82 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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