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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: 0.84
---
<!-- 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: 0.5771
- Accuracy: 0.84
## 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-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 10
- total_train_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1148 | 0.11 | 5 | 0.5865 | 0.83 |
| 0.1411 | 0.22 | 10 | 0.5951 | 0.83 |
| 0.1014 | 0.33 | 15 | 0.5964 | 0.83 |
| 0.085 | 0.44 | 20 | 0.5901 | 0.83 |
| 0.1362 | 0.56 | 25 | 0.5894 | 0.82 |
| 0.0917 | 0.67 | 30 | 0.5862 | 0.83 |
| 0.097 | 0.78 | 35 | 0.5759 | 0.84 |
| 0.1206 | 0.89 | 40 | 0.5701 | 0.84 |
| 0.0909 | 1.0 | 45 | 0.5649 | 0.84 |
| 0.1269 | 1.11 | 50 | 0.5674 | 0.84 |
| 0.1117 | 1.22 | 55 | 0.5714 | 0.84 |
| 0.0791 | 1.33 | 60 | 0.5730 | 0.86 |
| 0.1016 | 1.44 | 65 | 0.5745 | 0.84 |
| 0.0712 | 1.56 | 70 | 0.5744 | 0.85 |
| 0.1212 | 1.67 | 75 | 0.5773 | 0.85 |
| 0.0724 | 1.78 | 80 | 0.5782 | 0.85 |
| 0.0831 | 1.89 | 85 | 0.5777 | 0.85 |
| 0.1429 | 2.0 | 90 | 0.5771 | 0.84 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
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