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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: 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. -->
# 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: 0.5960
- 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2592 | 0.99 | 28 | 2.2167 | 0.25 |
| 1.8769 | 1.98 | 56 | 1.8139 | 0.49 |
| 1.5783 | 2.97 | 84 | 1.5107 | 0.61 |
| 1.3068 | 4.0 | 113 | 1.2779 | 0.68 |
| 1.1062 | 4.99 | 141 | 1.0318 | 0.8 |
| 1.0125 | 5.98 | 169 | 0.9156 | 0.83 |
| 0.8787 | 6.97 | 197 | 0.8099 | 0.86 |
| 0.7658 | 8.0 | 226 | 0.7804 | 0.85 |
| 0.7811 | 8.99 | 254 | 0.7448 | 0.83 |
| 0.6369 | 9.98 | 282 | 0.6841 | 0.84 |
| 0.4859 | 10.97 | 310 | 0.6353 | 0.85 |
| 0.4705 | 12.0 | 339 | 0.6193 | 0.87 |
| 0.4571 | 12.99 | 367 | 0.6090 | 0.86 |
| 0.3999 | 13.98 | 395 | 0.5912 | 0.86 |
| 0.4007 | 14.87 | 420 | 0.5960 | 0.85 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
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