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---
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: gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.88
license: apache-2.0
---
<!-- 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 on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.76
- 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: 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: 15
- .train_test_split(seed=2024, shuffle=True, test_size=0.1)
-
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 1.9415 | 1.0 | 113 | 0.55 | 1.8500 |
| 1.3078 | 2.0 | 226 | 0.58 | 1.3794 |
| 1.1238 | 3.0 | 339 | 0.65 | 1.0919 |
| 0.788 | 4.0 | 452 | 0.68 | 1.0212 |
| 0.5932 | 5.0 | 565 | 0.69 | 0.8691 |
| 0.4042 | 6.0 | 678 | 0.71 | 0.8527 |
| 0.3421 | 7.0 | 791 | 0.75 | 0.7737 |
| 0.223 | 8.0 | 904 | 0.75 | 0.8463 |
| 0.1162 | 9.0 | 1017 | 0.77 | 0.7808 |
| 0.0863 | 10.0 | 1130 | 0.75 | 0.7487 |
| 0.1357 | 11.0 | 1243 | 0.8839 | 0.76 |
| 0.0632 | 12.0 | 1356 | 0.7509 | 0.76 |
| 0.0342 | 13.0 | 1469 | 0.8219 | 0.77 |
| 0.0277 | 14.0 | 1582 | 0.7691 | 0.8 |
| 0.0307 | 15.0 | 1695 | 0.7854 | 0.77 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.13.2 |