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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: 1.1893
- 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: 4
- eval_batch_size: 4
- 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9486 | 1.0 | 225 | 1.8744 | 0.54 |
| 1.0616 | 2.0 | 450 | 1.2196 | 0.66 |
| 1.0193 | 3.0 | 675 | 0.7841 | 0.78 |
| 0.81 | 4.0 | 900 | 0.7212 | 0.8 |
| 0.2171 | 5.0 | 1125 | 0.7194 | 0.77 |
| 0.0458 | 6.0 | 1350 | 0.8966 | 0.81 |
| 0.3485 | 7.0 | 1575 | 0.7960 | 0.81 |
| 0.09 | 8.0 | 1800 | 1.0860 | 0.82 |
| 0.0031 | 9.0 | 2025 | 0.7744 | 0.84 |
| 0.0026 | 10.0 | 2250 | 0.8249 | 0.87 |
| 0.0032 | 11.0 | 2475 | 1.0680 | 0.84 |
| 0.0012 | 12.0 | 2700 | 1.0724 | 0.83 |
| 0.0011 | 13.0 | 2925 | 1.1407 | 0.83 |
| 0.0009 | 14.0 | 3150 | 1.0395 | 0.85 |
| 0.0007 | 15.0 | 3375 | 1.2991 | 0.83 |
| 0.0006 | 16.0 | 3600 | 1.1403 | 0.83 |
| 0.0007 | 17.0 | 3825 | 1.0837 | 0.83 |
| 0.0005 | 18.0 | 4050 | 1.1463 | 0.83 |
| 0.0005 | 19.0 | 4275 | 1.1987 | 0.83 |
| 0.0005 | 20.0 | 4500 | 1.1893 | 0.83 |
### Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4.dev0
- Tokenizers 0.13.3
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