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:
- Loss: 0.7713
- Accuracy: 0.87
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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.9891 | 0.99 | 56 | 1.9587 | 0.4 |
| 1.5271 | 2.0 | 113 | 1.4658 | 0.56 |
| 1.074 | 2.99 | 169 | 0.9198 | 0.79 |
| 0.8036 | 4.0 | 226 | 0.9191 | 0.7 |
| 0.5017 | 4.99 | 282 | 0.7299 | 0.8 |
| 0.3405 | 6.0 | 339 | 0.6682 | 0.8 |
| 0.2178 | 6.99 | 395 | 0.6877 | 0.82 |
| 0.116 | 8.0 | 452 | 0.6092 | 0.83 |
| 0.0616 | 8.99 | 508 | 0.6579 | 0.85 |
| 0.0229 | 10.0 | 565 | 0.8793 | 0.8 |
| 0.0128 | 10.99 | 621 | 0.6722 | 0.87 |
| 0.0094 | 12.0 | 678 | 0.7586 | 0.87 |
| 0.0073 | 12.99 | 734 | 0.7636 | 0.87 |
| 0.007 | 14.0 | 791 | 0.7728 | 0.87 |
| 0.0073 | 14.87 | 840 | 0.7713 | 0.87 |
Framework versions
- Transformers 4.30.2
- Pytorch 1.11.0+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1