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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: 0.86
---
<!-- 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: 0.8060
- Accuracy: 0.86
## 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: 1e-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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4315 | 1.0 | 7 | 0.5652 | 0.94 |
| 1.8803 | 2.0 | 14 | 0.5438 | 0.9 |
| 1.236 | 3.0 | 21 | 0.5891 | 0.9 |
| 0.6882 | 4.0 | 28 | 0.6378 | 0.9 |
| 0.6126 | 5.0 | 35 | 0.6965 | 0.88 |
| 0.6847 | 6.0 | 42 | 0.7258 | 0.84 |
| 0.3871 | 7.0 | 49 | 0.7459 | 0.84 |
| 0.2981 | 8.0 | 56 | 0.8024 | 0.86 |
| 0.1321 | 9.0 | 63 | 0.8140 | 0.86 |
| 0.2443 | 10.0 | 70 | 0.8060 | 0.86 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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