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: 0.85
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: 0.85
- Loss: 0.7531
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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|---|---|---|---|---|
| 2.2849 | 1.0 | 14 | 0.17 | 2.2588 |
| 2.1931 | 1.99 | 28 | 0.47 | 2.0874 |
| 1.9194 | 2.99 | 42 | 0.58 | 1.8044 |
| 1.6351 | 3.98 | 56 | 0.61 | 1.5806 |
| 1.4473 | 4.98 | 70 | 0.71 | 1.3886 |
| 1.3131 | 5.97 | 84 | 0.7 | 1.2738 |
| 1.2141 | 6.97 | 98 | 0.72 | 1.1616 |
| 1.0657 | 7.96 | 112 | 0.74 | 1.1272 |
| 0.96 | 8.96 | 126 | 0.75 | 1.0251 |
| 0.8387 | 9.96 | 140 | 0.8 | 0.9364 |
| 0.8653 | 10.95 | 154 | 0.79 | 0.8858 |
| 0.7653 | 11.95 | 168 | 0.8 | 0.8233 |
| 0.7329 | 12.94 | 182 | 0.83 | 0.7982 |
| 0.675 | 13.94 | 196 | 0.81 | 0.8189 |
| 0.6174 | 14.93 | 210 | 0.82 | 0.8236 |
| 0.5714 | 16.0 | 225 | 0.82 | 0.7755 |
| 0.598 | 17.0 | 239 | 0.81 | 0.7511 |
| 0.5794 | 17.99 | 253 | 0.84 | 0.7553 |
| 0.589 | 18.99 | 267 | 0.85 | 0.7533 |
| 0.5717 | 19.91 | 280 | 0.85 | 0.7531 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0