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: 0.77
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: 0.7624
- Accuracy: 0.77
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
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.169 | 1.0 | 57 | 2.0533 | 0.51 |
| 1.6117 | 2.0 | 114 | 1.5264 | 0.57 |
| 1.3112 | 3.0 | 171 | 1.2764 | 0.64 |
| 0.9584 | 4.0 | 228 | 1.0663 | 0.72 |
| 0.8809 | 5.0 | 285 | 0.9548 | 0.72 |
| 0.7652 | 6.0 | 342 | 0.9119 | 0.77 |
| 0.6498 | 7.0 | 399 | 0.8271 | 0.77 |
| 0.5007 | 8.0 | 456 | 0.7962 | 0.76 |
| 0.4747 | 9.0 | 513 | 0.7583 | 0.77 |
| 0.4418 | 10.0 | 570 | 0.7624 | 0.77 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1