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: 0.5214
- 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.735 | 1.0 | 113 | 1.7670 | 0.48 |
| 1.2344 | 2.0 | 226 | 1.2200 | 0.69 |
| 1.0264 | 3.0 | 339 | 0.8847 | 0.8 |
| 0.6698 | 4.0 | 452 | 0.7208 | 0.82 |
| 0.503 | 5.0 | 565 | 0.6785 | 0.78 |
| 0.3042 | 6.0 | 678 | 0.5969 | 0.84 |
| 0.2176 | 7.0 | 791 | 0.5525 | 0.86 |
| 0.3577 | 8.0 | 904 | 0.5487 | 0.85 |
| 0.137 | 9.0 | 1017 | 0.5064 | 0.87 |
| 0.1305 | 10.0 | 1130 | 0.5214 | 0.86 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3