metadata
license: apache-2.0
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
metrics:
- name: Accuracy
type: accuracy
value: 0.65
distilhubert-finetuned-gtzan
This model is a fine-tuned version of ntu-spml/distilhubert on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.4484
- Accuracy: 0.65
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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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: 8
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.2604 | 1.0 | 113 | 2.2157 | 0.38 |
| 1.972 | 2.0 | 226 | 1.9740 | 0.45 |
| 1.8073 | 3.0 | 339 | 1.7988 | 0.54 |
| 1.6946 | 4.0 | 452 | 1.6666 | 0.64 |
| 1.582 | 5.0 | 565 | 1.5732 | 0.61 |
| 1.6468 | 6.0 | 678 | 1.4905 | 0.65 |
| 1.4905 | 7.0 | 791 | 1.4661 | 0.65 |
| 1.4801 | 8.0 | 904 | 1.4484 | 0.65 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0