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: 0.5391
- 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: 4e-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
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
2.1051 | 1.0 | 113 | 2.1497 | 0.37 |
1.8425 | 2.0 | 226 | 1.8414 | 0.51 |
1.529 | 3.0 | 339 | 1.4372 | 0.64 |
1.1083 | 4.0 | 452 | 1.1113 | 0.74 |
0.8602 | 5.0 | 565 | 0.8216 | 0.79 |
0.5928 | 6.0 | 678 | 0.7559 | 0.77 |
0.3821 | 7.0 | 791 | 0.6388 | 0.81 |
0.4732 | 8.0 | 904 | 0.5476 | 0.86 |
0.303 | 9.0 | 1017 | 0.5160 | 0.9 |
0.3458 | 10.0 | 1130 | 0.5391 | 0.87 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
- Downloads last month
- 2
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.