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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: None
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.82
---
<!-- 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: 2.2594
- Accuracy: 0.82
## 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: 16
- eval_batch_size: 16
- 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: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2669 | 1.0 | 57 | 2.2222 | 0.29 |
| 1.9365 | 2.0 | 114 | 1.8485 | 0.53 |
| 1.5115 | 3.0 | 171 | 1.4544 | 0.64 |
| 1.1314 | 4.0 | 228 | 1.1404 | 0.7 |
| 0.9473 | 5.0 | 285 | 0.9750 | 0.7 |
| 0.8026 | 6.0 | 342 | 0.8381 | 0.76 |
| 0.669 | 7.0 | 399 | 0.7231 | 0.81 |
| 0.5026 | 8.0 | 456 | 0.7019 | 0.8 |
| 0.3179 | 9.0 | 513 | 0.6318 | 0.81 |
| 0.2934 | 10.0 | 570 | 0.6551 | 0.81 |
| 0.1709 | 11.0 | 627 | 0.6041 | 0.81 |
| 0.1502 | 12.0 | 684 | 0.7066 | 0.84 |
| 0.0626 | 13.0 | 741 | 0.6859 | 0.84 |
| 0.0184 | 14.0 | 798 | 0.7444 | 0.8 |
| 0.0345 | 15.0 | 855 | 0.9701 | 0.8 |
| 0.0034 | 16.0 | 912 | 1.0236 | 0.83 |
| 0.0014 | 17.0 | 969 | 1.1226 | 0.81 |
| 0.0811 | 18.0 | 1026 | 1.2570 | 0.81 |
| 0.0002 | 19.0 | 1083 | 1.3850 | 0.81 |
| 0.0 | 20.0 | 1140 | 1.6715 | 0.82 |
| 0.0 | 21.0 | 1197 | 1.8665 | 0.8 |
| 0.1033 | 22.0 | 1254 | 1.8919 | 0.79 |
| 0.047 | 23.0 | 1311 | 1.9730 | 0.82 |
| 0.0 | 24.0 | 1368 | 2.1126 | 0.81 |
| 0.0 | 25.0 | 1425 | 2.1545 | 0.79 |
| 0.0 | 26.0 | 1482 | 2.2609 | 0.79 |
| 0.0 | 27.0 | 1539 | 2.2284 | 0.81 |
| 0.0 | 28.0 | 1596 | 2.2374 | 0.81 |
| 0.0 | 29.0 | 1653 | 2.2590 | 0.82 |
| 0.0 | 30.0 | 1710 | 2.2594 | 0.82 |
### Framework versions
- Transformers 4.43.0.dev0
- Pytorch 2.3.1+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1
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