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---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- gtzan
metrics:
- accuracy
model-index:
- name: fine-tuned-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: 0.89
---
<!-- 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. -->
# fine-tuned-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: 0.5559
- Accuracy: 0.89
- Acc: 0.89
- F1: 0.89
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.946 | 1.0 | 112 | 1.7832 | 0.52 |
| 1.3084 | 2.0 | 225 | 1.2228 | 0.65 |
| 0.8713 | 3.0 | 337 | 0.8974 | 0.75 |
| 0.6942 | 4.0 | 450 | 0.9075 | 0.77 |
| 0.4072 | 5.0 | 562 | 0.7447 | 0.78 |
| 0.2349 | 6.0 | 675 | 0.6456 | 0.81 |
| 0.197 | 7.0 | 787 | 0.6228 | 0.8 |
| 0.2246 | 8.0 | 900 | 0.5927 | 0.84 |
| 0.1987 | 9.0 | 1012 | 0.5558 | 0.88 |
| 0.1152 | 9.96 | 1120 | 0.5559 | 0.89 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
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