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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: 0.84 |
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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: 0.5771 |
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- Accuracy: 0.84 |
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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-06 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 10 |
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- total_train_batch_size: 20 |
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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: 2 |
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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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| 0.1148 | 0.11 | 5 | 0.5865 | 0.83 | |
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| 0.1411 | 0.22 | 10 | 0.5951 | 0.83 | |
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| 0.1014 | 0.33 | 15 | 0.5964 | 0.83 | |
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| 0.085 | 0.44 | 20 | 0.5901 | 0.83 | |
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| 0.1362 | 0.56 | 25 | 0.5894 | 0.82 | |
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| 0.0917 | 0.67 | 30 | 0.5862 | 0.83 | |
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| 0.097 | 0.78 | 35 | 0.5759 | 0.84 | |
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| 0.1206 | 0.89 | 40 | 0.5701 | 0.84 | |
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| 0.0909 | 1.0 | 45 | 0.5649 | 0.84 | |
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| 0.1269 | 1.11 | 50 | 0.5674 | 0.84 | |
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| 0.1117 | 1.22 | 55 | 0.5714 | 0.84 | |
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| 0.0791 | 1.33 | 60 | 0.5730 | 0.86 | |
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| 0.1016 | 1.44 | 65 | 0.5745 | 0.84 | |
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| 0.0712 | 1.56 | 70 | 0.5744 | 0.85 | |
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| 0.1212 | 1.67 | 75 | 0.5773 | 0.85 | |
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| 0.0724 | 1.78 | 80 | 0.5782 | 0.85 | |
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| 0.0831 | 1.89 | 85 | 0.5777 | 0.85 | |
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| 0.1429 | 2.0 | 90 | 0.5771 | 0.84 | |
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### Framework versions |
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- Transformers 4.38.0.dev0 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
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