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: 0.6028
- 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-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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
Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|---|---|---|---|---|
| 2.123 | 0.99 | 56 | 0.34 | 2.0351 |
| 1.5133 | 2.0 | 113 | 0.63 | 1.4339 |
| 1.2081 | 2.99 | 169 | 0.7 | 1.1070 |
| 1.007 | 4.0 | 226 | 0.78 | 0.9590 |
| 0.7952 | 4.99 | 282 | 0.79 | 0.8661 |
| 0.6369 | 6.0 | 339 | 0.75 | 0.8490 |
| 0.5794 | 6.99 | 392 | 0.6839 | 0.82 |
| 0.4896 | 8.0 | 449 | 0.6623 | 0.84 |
| 0.5667 | 8.99 | 505 | 0.6228 | 0.83 |
| 0.46 | 9.96 | 560 | 0.6028 | 0.87 |
Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3