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: 0.5120
- Accuracy: 0.86
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: 4e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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: 20
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
2.2584 | 1.0 | 57 | 2.2062 | 0.35 |
1.8611 | 2.0 | 114 | 1.7924 | 0.53 |
1.4492 | 3.0 | 171 | 1.3901 | 0.65 |
1.0971 | 4.0 | 228 | 1.1676 | 0.69 |
0.9848 | 5.0 | 285 | 0.9750 | 0.74 |
0.8434 | 6.0 | 342 | 0.8434 | 0.74 |
0.7321 | 7.0 | 399 | 0.7555 | 0.83 |
0.5364 | 8.0 | 456 | 0.6995 | 0.79 |
0.4557 | 9.0 | 513 | 0.6118 | 0.84 |
0.4166 | 10.0 | 570 | 0.5975 | 0.83 |
0.2729 | 11.0 | 627 | 0.5576 | 0.83 |
0.2491 | 12.0 | 684 | 0.5737 | 0.82 |
0.2211 | 13.0 | 741 | 0.5129 | 0.84 |
0.1243 | 14.0 | 798 | 0.5710 | 0.83 |
0.0904 | 15.0 | 855 | 0.5087 | 0.86 |
0.0773 | 16.0 | 912 | 0.5836 | 0.8 |
0.0598 | 17.0 | 969 | 0.4871 | 0.83 |
0.0551 | 18.0 | 1026 | 0.4865 | 0.84 |
0.0467 | 19.0 | 1083 | 0.5043 | 0.84 |
0.0364 | 20.0 | 1140 | 0.5120 | 0.86 |
Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 3.0.0
- Tokenizers 0.19.1
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Model tree for sfedar/distilhubert-finetuned-gtzan
Base model
ntu-spml/distilhubert