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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: 0.79
---
<!-- 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: 0.7146
- Accuracy: 0.79
## 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: 12
- eval_batch_size: 12
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0711 | 1.0 | 75 | 1.9438 | 0.49 |
| 1.4944 | 2.0 | 150 | 1.4307 | 0.53 |
| 1.2562 | 3.0 | 225 | 1.2180 | 0.65 |
| 0.9436 | 4.0 | 300 | 1.0209 | 0.71 |
| 0.7543 | 5.0 | 375 | 0.9073 | 0.73 |
| 0.5742 | 6.0 | 450 | 0.8047 | 0.75 |
| 0.4728 | 7.0 | 525 | 0.7736 | 0.78 |
| 0.3622 | 8.0 | 600 | 0.7412 | 0.78 |
| 0.2447 | 9.0 | 675 | 0.7117 | 0.79 |
| 0.2692 | 10.0 | 750 | 0.7146 | 0.79 |
### Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
|