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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: 0.9791
- 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
- training_steps: 4000
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 2.0825 | 0.88 | 100 | 0.47 | 1.8392 |
| 1.4043 | 1.77 | 200 | 0.67 | 1.2675 |
| 1.0686 | 2.65 | 300 | 0.71 | 1.0186 |
| 0.8037 | 3.54 | 400 | 0.74 | 0.9198 |
| 0.6215 | 4.42 | 500 | 0.78 | 0.7636 |
| 0.5106 | 5.31 | 600 | 0.76 | 0.7937 |
| 0.3844 | 6.19 | 700 | 0.78 | 0.6909 |
| 0.3043 | 7.08 | 800 | 0.77 | 0.7279 |
| 0.2453 | 7.96 | 900 | 0.82 | 0.6447 |
| 0.211 | 8.85 | 1000 | 0.84 | 0.6404 |
| 0.2268 | 9.73 | 1100 | 0.77 | 0.7198 |
| 0.1565 | 10.62 | 1200 | 0.83 | 0.6704 |
| 0.0694 | 11.5 | 1300 | 0.83 | 0.8017 |
| 0.0568 | 12.39 | 1400 | 0.8 | 0.7841 |
| 0.0441 | 13.27 | 1500 | 0.81 | 0.7757 |
| 0.0302 | 14.16 | 1600 | 0.84 | 0.7819 |
| 0.0116 | 15.04 | 1700 | 0.83 | 0.7949 |
| 0.0289 | 15.93 | 1800 | 0.85 | 0.8057 |
| 0.0115 | 16.81 | 1900 | 0.83 | 0.8271 |
| 0.0081 | 17.7 | 2000 | 0.86 | 0.8005 |
| 0.0124 | 18.58 | 2100 | 0.8 | 0.8927 |
| 0.0219 | 19.47 | 2200 | 0.85 | 0.8126 |
| 0.0161 | 20.35 | 2300 | 0.85 | 0.8464 |
| 0.0157 | 21.24 | 2400 | 0.86 | 0.8459 |
| 0.0039 | 22.12 | 2500 | 0.8 | 1.0282 |
| 0.0157 | 23.01 | 2600 | 0.86 | 0.8649 |
| 0.0119 | 23.89 | 2700 | 0.85 | 0.8894 |
| 0.0129 | 24.78 | 2800 | 0.87 | 0.8624 |
| 0.0124 | 25.66 | 2900 | 0.85 | 0.8862 |
| 0.0025 | 26.55 | 3000 | 0.84 | 0.9097 |
| 0.0197 | 27.43 | 3100 | 0.9150 | 0.85 |
| 0.0193 | 28.32 | 3200 | 0.9986 | 0.83 |
| 0.0119 | 29.2 | 3300 | 0.9001 | 0.87 |
| 0.0017 | 30.09 | 3400 | 0.9599 | 0.83 |
| 0.015 | 30.97 | 3500 | 0.9442 | 0.84 |
| 0.0015 | 31.86 | 3600 | 0.9813 | 0.83 |
| 0.0056 | 32.74 | 3700 | 0.9791 | 0.83 |
### Framework versions
- Transformers 4.32.0
- Pytorch 1.12.1+cu113
- Datasets 2.14.4
- Tokenizers 0.13.3
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