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: 0.87
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: 1.0924
- 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: 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: 15
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.7495 | 1.0 | 450 | 1.7168 | 0.52 |
| 1.1633 | 2.0 | 900 | 1.0515 | 0.66 |
| 0.3792 | 3.0 | 1350 | 0.7312 | 0.73 |
| 0.5365 | 4.0 | 1800 | 0.9707 | 0.75 |
| 0.0234 | 5.0 | 2250 | 1.1124 | 0.75 |
| 0.0039 | 6.0 | 2700 | 0.9717 | 0.82 |
| 0.1781 | 7.0 | 3150 | 1.0491 | 0.82 |
| 0.0009 | 8.0 | 3600 | 1.1946 | 0.83 |
| 0.0007 | 9.0 | 4050 | 1.1116 | 0.84 |
| 0.0004 | 10.0 | 4500 | 1.0814 | 0.85 |
| 0.0004 | 11.0 | 4950 | 1.1160 | 0.85 |
| 0.0003 | 12.0 | 5400 | 1.1082 | 0.85 |
| 0.0003 | 13.0 | 5850 | 1.1311 | 0.86 |
| 0.0002 | 14.0 | 6300 | 1.1159 | 0.86 |
| 0.0003 | 15.0 | 6750 | 1.0924 | 0.87 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2