metadata
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.84
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: 1.0184
- Accuracy: 0.84
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
- 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 | Validation Loss | Accuracy |
---|---|---|---|---|
1.6293 | 1.0 | 450 | 1.4785 | 0.51 |
1.4503 | 2.0 | 900 | 1.0904 | 0.68 |
0.1918 | 3.0 | 1350 | 0.6702 | 0.75 |
0.0857 | 4.0 | 1800 | 0.7173 | 0.79 |
0.0601 | 5.0 | 2250 | 0.9274 | 0.77 |
0.0047 | 6.0 | 2700 | 0.9787 | 0.81 |
0.6662 | 7.0 | 3150 | 1.0511 | 0.81 |
0.0012 | 8.0 | 3600 | 1.0870 | 0.84 |
0.0015 | 9.0 | 4050 | 0.9564 | 0.87 |
0.0012 | 10.0 | 4500 | 1.0184 | 0.84 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2