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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: 0.87
---
<!-- 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: 1.1035
- Accuracy: 0.87
## 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: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0544 | 1.0 | 225 | 1.9608 | 0.47 |
| 1.2995 | 2.0 | 450 | 1.3852 | 0.51 |
| 0.8875 | 3.0 | 675 | 0.9288 | 0.71 |
| 0.4092 | 4.0 | 900 | 0.8114 | 0.76 |
| 0.5624 | 5.0 | 1125 | 0.8704 | 0.77 |
| 0.0609 | 6.0 | 1350 | 0.7951 | 0.82 |
| 0.1018 | 7.0 | 1575 | 0.7055 | 0.86 |
| 0.2941 | 8.0 | 1800 | 0.8832 | 0.83 |
| 0.0044 | 9.0 | 2025 | 0.9883 | 0.83 |
| 0.0025 | 10.0 | 2250 | 0.9306 | 0.88 |
| 0.0016 | 11.0 | 2475 | 0.9535 | 0.86 |
| 0.0012 | 12.0 | 2700 | 1.0921 | 0.85 |
| 0.001 | 13.0 | 2925 | 1.0428 | 0.86 |
| 0.0011 | 14.0 | 3150 | 1.2270 | 0.83 |
| 0.0008 | 15.0 | 3375 | 1.1831 | 0.84 |
| 0.0007 | 16.0 | 3600 | 1.2124 | 0.84 |
| 0.0007 | 17.0 | 3825 | 1.0806 | 0.86 |
| 0.2454 | 18.0 | 4050 | 1.1530 | 0.85 |
| 0.0006 | 19.0 | 4275 | 1.1078 | 0.86 |
| 0.0006 | 20.0 | 4500 | 1.1035 | 0.87 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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