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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: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.83
---
<!-- 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: 0.6468
- Accuracy: 0.83
## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9254 | 1.0 | 113 | 1.8282 | 0.45 |
| 1.2779 | 2.0 | 226 | 1.2286 | 0.67 |
| 0.9885 | 3.0 | 339 | 1.0514 | 0.73 |
| 0.6829 | 4.0 | 452 | 0.8782 | 0.7 |
| 0.5083 | 5.0 | 565 | 0.6562 | 0.81 |
| 0.3546 | 6.0 | 678 | 0.6958 | 0.79 |
| 0.22 | 7.0 | 791 | 0.6093 | 0.81 |
| 0.1039 | 8.0 | 904 | 0.6242 | 0.81 |
| 0.1092 | 9.0 | 1017 | 0.6058 | 0.83 |
| 0.066 | 10.0 | 1130 | 0.6468 | 0.83 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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