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: 0.8
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: 0.6313
- Accuracy: 0.8
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: 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: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.8947 | 1.0 | 113 | 1.8216 | 0.49 |
| 1.2821 | 2.0 | 226 | 1.2771 | 0.65 |
| 0.9869 | 3.0 | 339 | 1.1200 | 0.64 |
| 0.729 | 4.0 | 452 | 0.9418 | 0.73 |
| 0.486 | 5.0 | 565 | 0.6837 | 0.78 |
| 0.3599 | 6.0 | 678 | 0.6319 | 0.83 |
| 0.255 | 7.0 | 791 | 0.6670 | 0.78 |
| 0.1186 | 8.0 | 904 | 0.6201 | 0.79 |
| 0.1559 | 9.0 | 1017 | 0.6294 | 0.79 |
| 0.098 | 10.0 | 1130 | 0.6313 | 0.8 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1