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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: distilhubert-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.79
---
<!-- 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. -->
# distilhubert-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: 0.6405
- Accuracy: 0.79
## 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: 6
- eval_batch_size: 6
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9208 | 1.0 | 150 | 1.7528 | 0.52 |
| 1.0745 | 2.0 | 300 | 1.2385 | 0.6 |
| 0.8249 | 3.0 | 450 | 0.8622 | 0.79 |
| 0.6652 | 4.0 | 600 | 0.9211 | 0.72 |
| 0.4782 | 5.0 | 750 | 0.6200 | 0.8 |
| 0.2865 | 6.0 | 900 | 0.6526 | 0.76 |
| 0.1781 | 7.0 | 1050 | 0.5741 | 0.82 |
| 0.1675 | 8.0 | 1200 | 0.5487 | 0.82 |
| 0.0497 | 9.0 | 1350 | 0.6100 | 0.8 |
| 0.0813 | 10.0 | 1500 | 0.6405 | 0.79 |
### Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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