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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: 0.85
---
<!-- 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: 0.9971
- Accuracy: 0.85
## 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: 3000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2793 | 0.88 | 100 | 2.1792 | 0.41 |
| 1.992 | 1.77 | 200 | 1.6741 | 0.56 |
| 1.4928 | 2.65 | 300 | 1.2795 | 0.56 |
| 1.1156 | 3.54 | 400 | 0.9983 | 0.69 |
| 0.9162 | 4.42 | 500 | 0.8222 | 0.73 |
| 0.6785 | 5.31 | 600 | 0.8422 | 0.78 |
| 0.4695 | 6.19 | 700 | 0.7034 | 0.8 |
| 0.3362 | 7.08 | 800 | 0.9594 | 0.72 |
| 0.2051 | 7.96 | 900 | 0.6157 | 0.84 |
| 0.1242 | 8.85 | 1000 | 0.6059 | 0.86 |
| 0.0678 | 9.73 | 1100 | 0.7626 | 0.86 |
| 0.0479 | 10.62 | 1200 | 0.7886 | 0.84 |
| 0.0216 | 11.5 | 1300 | 0.8302 | 0.85 |
| 0.0202 | 12.39 | 1400 | 0.8921 | 0.86 |
| 0.0155 | 13.27 | 1500 | 0.9971 | 0.85 |
### Framework versions
- Transformers 4.32.0
- Pytorch 1.12.1+cu113
- Datasets 2.14.4
- Tokenizers 0.13.3
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