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---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: music-genre-classifer-20-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
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# music-genre-classifer-20-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9000
- 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: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1605 | 1.0 | 113 | 2.0687 | 0.43 |
| 1.4882 | 2.0 | 226 | 1.4799 | 0.57 |
| 1.153 | 3.0 | 339 | 1.0688 | 0.72 |
| 0.7342 | 4.0 | 452 | 0.8125 | 0.77 |
| 0.6006 | 5.0 | 565 | 0.6751 | 0.81 |
| 0.3723 | 6.0 | 678 | 0.6401 | 0.8 |
| 0.2502 | 7.0 | 791 | 0.6562 | 0.8 |
| 0.1122 | 8.0 | 904 | 0.7022 | 0.81 |
| 0.0708 | 9.0 | 1017 | 0.6970 | 0.85 |
| 0.0181 | 10.0 | 1130 | 0.8265 | 0.84 |
| 0.0129 | 11.0 | 1243 | 0.7396 | 0.85 |
| 0.0061 | 12.0 | 1356 | 0.7550 | 0.87 |
| 0.0047 | 13.0 | 1469 | 0.8028 | 0.85 |
| 0.004 | 14.0 | 1582 | 0.8657 | 0.84 |
| 0.0039 | 15.0 | 1695 | 0.8750 | 0.85 |
| 0.0031 | 16.0 | 1808 | 0.8898 | 0.86 |
| 0.0028 | 17.0 | 1921 | 0.8835 | 0.85 |
| 0.0025 | 18.0 | 2034 | 0.9102 | 0.85 |
| 0.0025 | 19.0 | 2147 | 0.9007 | 0.86 |
| 0.0024 | 20.0 | 2260 | 0.9000 | 0.85 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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