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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 |
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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: 0.82 |
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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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# 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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- Loss: 2.2594 |
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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: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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 | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 2.2669 | 1.0 | 57 | 2.2222 | 0.29 | |
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| 1.9365 | 2.0 | 114 | 1.8485 | 0.53 | |
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| 1.5115 | 3.0 | 171 | 1.4544 | 0.64 | |
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| 1.1314 | 4.0 | 228 | 1.1404 | 0.7 | |
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| 0.9473 | 5.0 | 285 | 0.9750 | 0.7 | |
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| 0.8026 | 6.0 | 342 | 0.8381 | 0.76 | |
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| 0.669 | 7.0 | 399 | 0.7231 | 0.81 | |
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| 0.5026 | 8.0 | 456 | 0.7019 | 0.8 | |
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| 0.3179 | 9.0 | 513 | 0.6318 | 0.81 | |
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| 0.2934 | 10.0 | 570 | 0.6551 | 0.81 | |
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| 0.1709 | 11.0 | 627 | 0.6041 | 0.81 | |
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| 0.1502 | 12.0 | 684 | 0.7066 | 0.84 | |
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| 0.0626 | 13.0 | 741 | 0.6859 | 0.84 | |
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| 0.0184 | 14.0 | 798 | 0.7444 | 0.8 | |
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| 0.0345 | 15.0 | 855 | 0.9701 | 0.8 | |
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| 0.0034 | 16.0 | 912 | 1.0236 | 0.83 | |
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| 0.0014 | 17.0 | 969 | 1.1226 | 0.81 | |
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| 0.0811 | 18.0 | 1026 | 1.2570 | 0.81 | |
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| 0.0002 | 19.0 | 1083 | 1.3850 | 0.81 | |
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| 0.0 | 20.0 | 1140 | 1.6715 | 0.82 | |
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| 0.0 | 21.0 | 1197 | 1.8665 | 0.8 | |
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| 0.1033 | 22.0 | 1254 | 1.8919 | 0.79 | |
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| 0.047 | 23.0 | 1311 | 1.9730 | 0.82 | |
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| 0.0 | 24.0 | 1368 | 2.1126 | 0.81 | |
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| 0.0 | 25.0 | 1425 | 2.1545 | 0.79 | |
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| 0.0 | 26.0 | 1482 | 2.2609 | 0.79 | |
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| 0.0 | 27.0 | 1539 | 2.2284 | 0.81 | |
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| 0.0 | 28.0 | 1596 | 2.2374 | 0.81 | |
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| 0.0 | 29.0 | 1653 | 2.2590 | 0.82 | |
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| 0.0 | 30.0 | 1710 | 2.2594 | 0.82 | |
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### Framework versions |
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- Transformers 4.43.0.dev0 |
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- Pytorch 2.3.1+cu118 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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