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---
library_name: transformers
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: None
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.84
---
<!-- 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: 0.4772
- Accuracy: 0.88
## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1579 | 1.0 | 113 | 2.0599 | 0.49 |
| 1.414 | 2.0 | 226 | 1.4245 | 0.66 |
| 1.1784 | 3.0 | 339 | 1.1034 | 0.65 |
| 0.7209 | 4.0 | 452 | 0.7965 | 0.78 |
| 0.5967 | 5.0 | 565 | 0.7356 | 0.78 |
| 0.3644 | 6.0 | 678 | 0.5675 | 0.82 |
| 0.2092 | 7.0 | 791 | 0.5001 | 0.86 |
| 0.0647 | 8.0 | 904 | 0.6535 | 0.83 |
| 0.1354 | 9.0 | 1017 | 0.4772 | 0.88 |
| 0.0525 | 10.0 | 1130 | 0.6393 | 0.86 |
| 0.0127 | 11.0 | 1243 | 0.6587 | 0.87 |
| 0.0059 | 12.0 | 1356 | 0.6669 | 0.84 |
| 0.0052 | 13.0 | 1469 | 0.6354 | 0.86 |
| 0.0044 | 14.0 | 1582 | 0.7918 | 0.82 |
| 0.0035 | 15.0 | 1695 | 0.7577 | 0.83 |
| 0.0035 | 16.0 | 1808 | 0.7344 | 0.82 |
| 0.0033 | 17.0 | 1921 | 0.7079 | 0.84 |
| 0.0031 | 18.0 | 2034 | 0.7367 | 0.84 |
| 0.0032 | 19.0 | 2147 | 0.7150 | 0.84 |
| 0.0034 | 20.0 | 2260 | 0.7144 | 0.84 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
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