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: 0.85
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: 0.5407
- Accuracy: 0.85
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 |
|---|---|---|---|---|
| 1.9048 | 1.0 | 113 | 1.8733 | 0.46 |
| 1.2144 | 2.0 | 226 | 1.2187 | 0.68 |
| 1.0517 | 3.0 | 339 | 0.9966 | 0.72 |
| 0.6106 | 4.0 | 452 | 0.8277 | 0.73 |
| 0.5758 | 5.0 | 565 | 0.6469 | 0.82 |
| 0.3493 | 6.0 | 678 | 0.5659 | 0.85 |
| 0.2218 | 7.0 | 791 | 0.5248 | 0.86 |
| 0.1377 | 8.0 | 904 | 0.5846 | 0.82 |
| 0.1254 | 9.0 | 1017 | 0.5229 | 0.87 |
| 0.0817 | 10.0 | 1130 | 0.5407 | 0.85 |
Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1