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: 0.83
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: 0.5930
- Accuracy: 0.83
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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.1006 | 1.0 | 57 | 1.9678 | 0.39 |
| 1.5415 | 2.0 | 114 | 1.3971 | 0.7 |
| 1.1393 | 3.0 | 171 | 1.1602 | 0.7 |
| 1.0024 | 4.0 | 228 | 0.9230 | 0.77 |
| 1.0193 | 5.0 | 285 | 0.7550 | 0.82 |
| 0.5975 | 6.0 | 342 | 0.7155 | 0.83 |
| 0.608 | 7.0 | 399 | 0.6451 | 0.83 |
| 0.3833 | 8.0 | 456 | 0.6135 | 0.83 |
| 0.467 | 9.0 | 513 | 0.5864 | 0.84 |
| 0.3761 | 10.0 | 570 | 0.5930 | 0.83 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1
- Datasets 2.14.4
- Tokenizers 0.13.3