distilhubert-finetuned-gtzan
This model is a fine-tuned version of ntu-spml/distilhubert on the GTZAN dataset. It achieves the following results on the evaluation set:
- Loss: 1.0125
- Accuracy: 0.73
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.3647 | 1.0 | 899 | 1.2020 | 0.65 |
| 1.0999 | 2.0 | 1798 | 1.1490 | 0.7 |
| 0.1482 | 3.0 | 2697 | 1.0125 | 0.73 |
Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.17.1
- Tokenizers 0.19.1
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Model tree for MacByner/distilhubert-finetuned-gtzan
Base model
ntu-spml/distilhubertDataset used to train MacByner/distilhubert-finetuned-gtzan
Evaluation results
- Accuracy on GTZANself-reported0.730