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: 0.8
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: 0.8555
- Accuracy: 0.8
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: 14
- eval_batch_size: 14
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 28
- 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 |
---|---|---|---|---|
2.238 | 0.9846 | 32 | 2.1541 | 0.41 |
1.7746 | 2.0 | 65 | 1.7033 | 0.6 |
1.4861 | 2.9846 | 97 | 1.4707 | 0.59 |
1.2082 | 4.0 | 130 | 1.2747 | 0.69 |
1.1266 | 4.9846 | 162 | 1.0837 | 0.76 |
0.9531 | 6.0 | 195 | 1.0155 | 0.72 |
0.9083 | 6.9846 | 227 | 0.9587 | 0.76 |
0.8255 | 8.0 | 260 | 0.8855 | 0.8 |
0.7696 | 8.9846 | 292 | 0.8766 | 0.79 |
0.7646 | 9.8462 | 320 | 0.8555 | 0.8 |
Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1