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: 0.88
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:
- Accuracy: 0.88
- Loss: 0.5101
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: 16
- eval_batch_size: 16
- 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: 19
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|---|---|---|---|---|
| 2.1142 | 1.0 | 57 | 0.5 | 1.9842 |
| 1.5086 | 2.0 | 114 | 0.63 | 1.4646 |
| 1.1112 | 3.0 | 171 | 0.76 | 1.1176 |
| 1.0085 | 4.0 | 228 | 0.74 | 0.9412 |
| 0.7851 | 5.0 | 285 | 0.8 | 0.7978 |
| 0.6372 | 6.0 | 342 | 0.78 | 0.7533 |
| 0.5404 | 7.0 | 399 | 0.75 | 0.7206 |
| 0.4701 | 8.0 | 456 | 0.8 | 0.6551 |
| 0.4362 | 9.0 | 513 | 0.77 | 0.6712 |
| 0.3737 | 10.0 | 570 | 0.81 | 0.6202 |
| 0.321 | 11.0 | 627 | 0.78 | 0.6756 |
| 0.2533 | 12.0 | 684 | 0.84 | 0.5602 |
| 0.326 | 13.0 | 741 | 0.84 | 0.5706 |
| 0.1789 | 14.0 | 798 | 0.83 | 0.5736 |
| 0.1841 | 15.0 | 855 | 0.85 | 0.5379 |
| 0.2496 | 16.0 | 912 | 0.87 | 0.5518 |
| 0.2002 | 17.0 | 969 | 0.86 | 0.5220 |
| 0.1164 | 18.0 | 1026 | 0.86 | 0.5213 |
| 0.096 | 19.0 | 1083 | 0.88 | 0.5101 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.3.0
- Datasets 2.19.0
- Tokenizers 0.15.1