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:
- Loss: 0.4795
- Accuracy: 0.88
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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 12
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.0422 | 1.0 | 225 | 2.0126 | 0.27 |
| 1.331 | 2.0 | 450 | 1.3795 | 0.54 |
| 1.2571 | 3.0 | 675 | 1.0070 | 0.72 |
| 1.2968 | 4.0 | 900 | 0.8590 | 0.77 |
| 0.7658 | 5.0 | 1125 | 0.7889 | 0.77 |
| 0.5499 | 6.0 | 1350 | 0.5743 | 0.82 |
| 0.8344 | 7.0 | 1575 | 0.6065 | 0.81 |
| 0.3919 | 8.0 | 1800 | 0.5650 | 0.87 |
| 0.2808 | 9.0 | 2025 | 0.4605 | 0.87 |
| 0.4463 | 10.0 | 2250 | 0.5161 | 0.86 |
| 0.5678 | 11.0 | 2475 | 0.5359 | 0.87 |
| 0.3032 | 12.0 | 2700 | 0.4795 | 0.88 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.0
- Datasets 2.14.3
- Tokenizers 0.13.3