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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-v2
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-v2
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: 1.5298
- 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: 8e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1489 | 1.0 | 113 | 2.2978 | 0.74 |
| 0.0001 | 2.0 | 226 | 2.2070 | 0.78 |
| 0.3174 | 3.0 | 339 | 1.7906 | 0.8 |
| 0.0001 | 4.0 | 452 | 1.5376 | 0.81 |
| 0.0 | 5.0 | 565 | 1.4012 | 0.85 |
| 0.0001 | 6.0 | 678 | 1.2597 | 0.87 |
| 0.0001 | 7.0 | 791 | 1.5363 | 0.86 |
| 0.0001 | 8.0 | 904 | 1.5298 | 0.86 |
| 0.0 | 9.0 | 1017 | 1.5277 | 0.86 |
| 0.0 | 10.0 | 1130 | 1.5298 | 0.86 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
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