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: 0.78
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: 0.9511
- Accuracy: 0.78
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
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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 |
|---|---|---|---|---|
| 0.6478 | 0.9912 | 56 | 0.7848 | 0.77 |
| 0.4009 | 2.0 | 113 | 0.8213 | 0.73 |
| 0.2155 | 2.9912 | 169 | 0.7877 | 0.76 |
| 0.1813 | 4.0 | 226 | 0.8529 | 0.75 |
| 0.0851 | 4.9912 | 282 | 0.8632 | 0.73 |
| 0.063 | 6.0 | 339 | 0.9026 | 0.78 |
| 0.0372 | 6.9912 | 395 | 0.8418 | 0.8 |
| 0.021 | 8.0 | 452 | 0.8672 | 0.79 |
| 0.0113 | 8.9912 | 508 | 0.9186 | 0.79 |
| 0.0098 | 9.9115 | 560 | 0.9511 | 0.78 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1