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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: ts-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: 0.83 |
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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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# ts-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: 0.6580 |
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- Accuracy: 0.83 |
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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: 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: 10 |
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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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| 1.9712 | 1.0 | 113 | 1.9122 | 0.47 | |
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| 1.2031 | 2.0 | 226 | 1.3221 | 0.61 | |
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| 0.9693 | 3.0 | 339 | 0.9988 | 0.72 | |
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| 0.871 | 4.0 | 452 | 0.8685 | 0.77 | |
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| 0.4698 | 5.0 | 565 | 0.7312 | 0.81 | |
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| 0.4306 | 6.0 | 678 | 0.7236 | 0.78 | |
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| 0.2482 | 7.0 | 791 | 0.8157 | 0.76 | |
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| 0.2672 | 8.0 | 904 | 0.5917 | 0.85 | |
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| 0.1592 | 9.0 | 1017 | 0.6369 | 0.83 | |
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| 0.1181 | 10.0 | 1130 | 0.6580 | 0.83 | |
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### Framework versions |
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- Transformers 4.36.0 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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