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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: 0.5833
- 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-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 5
- total_train_batch_size: 10
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9977 | 1.0 | 90 | 1.8501 | 0.47 |
| 1.2442 | 2.0 | 180 | 1.2525 | 0.65 |
| 1.1725 | 3.0 | 270 | 1.1111 | 0.68 |
| 0.955 | 4.0 | 360 | 0.8526 | 0.74 |
| 0.7524 | 5.0 | 450 | 0.7258 | 0.77 |
| 0.5618 | 6.0 | 540 | 0.7356 | 0.75 |
| 0.3265 | 7.0 | 630 | 0.6126 | 0.78 |
| 0.3194 | 8.0 | 720 | 0.5614 | 0.84 |
| 0.3098 | 9.0 | 810 | 0.5797 | 0.81 |
| 0.3189 | 10.0 | 900 | 0.5833 | 0.84 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1
- Datasets 2.4.0
- Tokenizers 0.13.3
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