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: 0.86
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: 0.5269
- Accuracy: 0.86
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: 0.0001
- 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
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.9286 | 0.99 | 56 | 1.7845 | 0.56 |
| 1.2162 | 2.0 | 113 | 1.1310 | 0.74 |
| 0.8715 | 2.99 | 169 | 0.8334 | 0.75 |
| 0.6735 | 4.0 | 226 | 0.7352 | 0.79 |
| 0.4007 | 4.99 | 282 | 0.5135 | 0.87 |
| 0.241 | 6.0 | 339 | 0.7801 | 0.76 |
| 0.181 | 6.99 | 395 | 0.5440 | 0.81 |
| 0.1336 | 8.0 | 452 | 0.5280 | 0.85 |
| 0.0526 | 8.99 | 508 | 0.4992 | 0.87 |
| 0.0315 | 9.91 | 560 | 0.5269 | 0.86 |
Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1
- Datasets 2.13.1
- Tokenizers 0.13.3