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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-2
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: 0.86
---
<!-- 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-2
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: 0.7203
- Accuracy: 0.86
## 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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2521 | 1.0 | 90 | 2.2219 | 0.3 |
| 1.8502 | 2.0 | 180 | 1.8299 | 0.54 |
| 1.4155 | 3.0 | 270 | 1.4247 | 0.64 |
| 0.9885 | 4.0 | 360 | 1.0313 | 0.7 |
| 0.8111 | 5.0 | 450 | 0.8535 | 0.78 |
| 0.7023 | 6.0 | 540 | 0.7743 | 0.79 |
| 0.5663 | 7.0 | 630 | 0.6618 | 0.81 |
| 0.3577 | 8.0 | 720 | 0.6937 | 0.77 |
| 0.3003 | 9.0 | 810 | 0.6107 | 0.82 |
| 0.1321 | 10.0 | 900 | 0.5648 | 0.81 |
| 0.0488 | 11.0 | 990 | 0.5655 | 0.84 |
| 0.0323 | 12.0 | 1080 | 0.5612 | 0.86 |
| 0.0154 | 13.0 | 1170 | 0.6338 | 0.85 |
| 0.0108 | 14.0 | 1260 | 0.7292 | 0.84 |
| 0.0082 | 15.0 | 1350 | 0.7542 | 0.84 |
| 0.0065 | 16.0 | 1440 | 0.7123 | 0.86 |
| 0.0062 | 17.0 | 1530 | 0.6949 | 0.86 |
| 0.0848 | 18.0 | 1620 | 0.7332 | 0.85 |
| 0.0053 | 19.0 | 1710 | 0.7291 | 0.85 |
| 0.005 | 20.0 | 1800 | 0.7203 | 0.86 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.2.1
- Datasets 2.17.1
- Tokenizers 0.15.2
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