metadata
library_name: transformers
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- gtzan
metrics:
- accuracy
model-index:
- name: ft-hubert-on-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: gtzan
type: gtzan
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.825
ft-hubert-on-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: 0.6574
- Accuracy: 0.825
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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 100 | 1.5408 | 0.58 |
| No log | 2.0 | 200 | 1.1600 | 0.615 |
| No log | 3.0 | 300 | 0.9942 | 0.705 |
| No log | 4.0 | 400 | 0.8390 | 0.77 |
| 1.0814 | 5.0 | 500 | 0.8495 | 0.745 |
| 1.0814 | 6.0 | 600 | 0.6807 | 0.79 |
| 1.0814 | 7.0 | 700 | 0.7361 | 0.78 |
| 1.0814 | 8.0 | 800 | 0.6250 | 0.815 |
| 1.0814 | 9.0 | 900 | 0.6308 | 0.83 |
| 0.2344 | 10.0 | 1000 | 0.6574 | 0.825 |
Framework versions
- Transformers 4.48.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0