metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
results: []
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:
- Accuracy: 0.87
- Loss: 0.6300
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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|---|---|---|---|---|
| 1.8192 | 1.0 | 150 | 0.41 | 1.7706 |
| 1.1745 | 2.0 | 300 | 0.64 | 1.2586 |
| 0.7885 | 3.0 | 450 | 0.76 | 0.8419 |
| 0.661 | 4.0 | 600 | 0.81 | 0.7019 |
| 0.3334 | 5.0 | 750 | 0.84 | 0.5766 |
| 0.3265 | 6.0 | 900 | 0.83 | 0.5862 |
| 0.0928 | 7.0 | 1050 | 0.88 | 0.5613 |
| 0.0698 | 8.0 | 1200 | 0.86 | 0.6432 |
| 0.0562 | 9.0 | 1350 | 0.87 | 0.6141 |
| 0.0268 | 10.0 | 1500 | 0.87 | 0.6300 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1
- Datasets 2.13.1
- Tokenizers 0.13.3