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: 0.83
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: 0.7428
- Accuracy: 0.83
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: 4
- eval_batch_size: 4
- 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
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.7684 | 1.0 | 225 | 1.6143 | 0.46 |
| 0.9707 | 2.0 | 450 | 1.0938 | 0.66 |
| 0.8819 | 3.0 | 675 | 0.7981 | 0.77 |
| 0.6527 | 4.0 | 900 | 0.6805 | 0.8 |
| 0.2499 | 5.0 | 1125 | 0.5896 | 0.81 |
| 0.0371 | 6.0 | 1350 | 0.8279 | 0.79 |
| 0.1651 | 7.0 | 1575 | 0.6830 | 0.81 |
| 0.011 | 8.0 | 1800 | 0.7673 | 0.81 |
| 0.0077 | 9.0 | 2025 | 0.7159 | 0.83 |
| 0.0068 | 10.0 | 2250 | 0.7428 | 0.83 |
Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3