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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: 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: 0.81 |
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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: 1.0178 |
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- Accuracy: 0.81 |
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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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| 2.2598 | 1.0 | 57 | 2.2140 | 0.34 | |
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| 1.8981 | 2.0 | 114 | 1.8262 | 0.56 | |
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| 1.4487 | 3.0 | 171 | 1.4402 | 0.64 | |
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| 1.1792 | 4.0 | 228 | 1.1520 | 0.69 | |
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| 0.9231 | 5.0 | 285 | 0.9415 | 0.75 | |
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| 0.7141 | 6.0 | 342 | 0.8904 | 0.73 | |
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| 0.5477 | 7.0 | 399 | 0.7395 | 0.78 | |
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| 0.3968 | 8.0 | 456 | 0.6359 | 0.81 | |
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| 0.4259 | 9.0 | 513 | 0.6345 | 0.8 | |
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| 0.2474 | 10.0 | 570 | 0.6333 | 0.8 | |
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| 0.1379 | 11.0 | 627 | 0.5374 | 0.83 | |
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| 0.0781 | 12.0 | 684 | 0.6484 | 0.84 | |
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| 0.0337 | 13.0 | 741 | 0.7072 | 0.84 | |
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| 0.0211 | 14.0 | 798 | 0.7023 | 0.83 | |
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| 0.0135 | 15.0 | 855 | 0.8199 | 0.83 | |
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| 0.0097 | 16.0 | 912 | 0.8009 | 0.83 | |
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| 0.065 | 17.0 | 969 | 0.8992 | 0.81 | |
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| 0.0067 | 18.0 | 1026 | 0.8628 | 0.82 | |
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| 0.0118 | 19.0 | 1083 | 0.6922 | 0.85 | |
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| 0.0052 | 20.0 | 1140 | 0.8001 | 0.84 | |
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| 0.077 | 21.0 | 1197 | 0.8324 | 0.82 | |
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| 0.0043 | 22.0 | 1254 | 0.9468 | 0.8 | |
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| 0.0039 | 23.0 | 1311 | 0.8866 | 0.8 | |
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| 0.0696 | 24.0 | 1368 | 0.9424 | 0.82 | |
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| 0.0037 | 25.0 | 1425 | 0.7855 | 0.81 | |
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| 0.0631 | 26.0 | 1482 | 0.7659 | 0.82 | |
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| 0.0592 | 27.0 | 1539 | 0.8605 | 0.83 | |
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| 0.0034 | 28.0 | 1596 | 0.9266 | 0.82 | |
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| 0.0032 | 29.0 | 1653 | 0.9831 | 0.82 | |
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| 0.0032 | 30.0 | 1710 | 1.0178 | 0.81 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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