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---
license: apache-2.0
base_model: gnuevo/distilhubert-finetuned-gtzan
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan-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-finetuned-gtzan
This model is a fine-tuned version of [gnuevo/distilhubert-finetuned-gtzan](https://huggingface.co/gnuevo/distilhubert-finetuned-gtzan) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9179
- 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.15
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.3025 | 1.0 | 57 | 2.2928 | 0.2 |
| 2.2609 | 2.0 | 114 | 2.2683 | 0.27 |
| 1.9479 | 3.0 | 171 | 1.9099 | 0.4 |
| 1.211 | 4.0 | 228 | 1.5258 | 0.39 |
| 0.9834 | 5.0 | 285 | 1.4254 | 0.52 |
| 0.6456 | 6.0 | 342 | 1.3216 | 0.57 |
| 0.5043 | 7.0 | 399 | 1.1890 | 0.69 |
| 0.4696 | 8.0 | 456 | 1.0764 | 0.8 |
| 0.3204 | 9.0 | 513 | 0.9564 | 0.82 |
| 0.3164 | 10.0 | 570 | 0.9101 | 0.83 |
| 0.2334 | 11.0 | 627 | 0.9021 | 0.84 |
| 0.217 | 12.0 | 684 | 0.9051 | 0.84 |
| 0.1781 | 13.0 | 741 | 0.9118 | 0.84 |
| 0.1203 | 14.0 | 798 | 0.9153 | 0.85 |
| 0.0639 | 15.0 | 855 | 0.9179 | 0.84 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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