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---
license: apache-2.0
base_model: Sandiago21/distilhubert-finetuned-gtzan
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: 0.88
---
<!-- 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 [Sandiago21/distilhubert-finetuned-gtzan](https://huggingface.co/Sandiago21/distilhubert-finetuned-gtzan) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9951
- Accuracy: 0.88
## 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: 0.0001
- 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: 0.1
- num_epochs: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0951 | 1.0 | 57 | 0.5566 | 0.87 |
| 0.0629 | 2.0 | 114 | 0.6819 | 0.83 |
| 0.0231 | 3.0 | 171 | 0.6118 | 0.86 |
| 0.0159 | 4.0 | 228 | 0.9208 | 0.83 |
| 0.0374 | 5.0 | 285 | 0.8746 | 0.85 |
| 0.1714 | 6.0 | 342 | 0.6671 | 0.87 |
| 0.2148 | 7.0 | 399 | 1.1850 | 0.79 |
| 0.0147 | 8.0 | 456 | 1.0551 | 0.79 |
| 0.0788 | 9.0 | 513 | 1.5179 | 0.79 |
| 0.0015 | 10.0 | 570 | 1.3290 | 0.8 |
| 0.0049 | 11.0 | 627 | 1.0943 | 0.85 |
| 0.0012 | 12.0 | 684 | 1.0667 | 0.85 |
| 0.0043 | 13.0 | 741 | 1.1816 | 0.82 |
| 0.0015 | 14.0 | 798 | 0.9108 | 0.88 |
| 0.0011 | 15.0 | 855 | 1.0289 | 0.87 |
| 0.001 | 16.0 | 912 | 0.7696 | 0.87 |
| 0.0006 | 17.0 | 969 | 0.8539 | 0.87 |
| 0.1001 | 18.0 | 1026 | 1.1917 | 0.78 |
| 0.0017 | 19.0 | 1083 | 1.0016 | 0.83 |
| 0.0525 | 20.0 | 1140 | 0.9513 | 0.88 |
| 0.0004 | 21.0 | 1197 | 0.9268 | 0.86 |
| 0.0003 | 22.0 | 1254 | 1.1209 | 0.82 |
| 0.0003 | 23.0 | 1311 | 0.9270 | 0.87 |
| 0.0003 | 24.0 | 1368 | 1.1148 | 0.84 |
| 0.0003 | 25.0 | 1425 | 1.0507 | 0.85 |
| 0.0002 | 26.0 | 1482 | 1.0156 | 0.86 |
| 0.0002 | 27.0 | 1539 | 1.0062 | 0.87 |
| 0.0002 | 28.0 | 1596 | 1.0124 | 0.87 |
| 0.0002 | 29.0 | 1653 | 1.0154 | 0.87 |
| 0.0002 | 30.0 | 1710 | 1.0092 | 0.88 |
| 0.0002 | 31.0 | 1767 | 1.0123 | 0.88 |
| 0.0175 | 32.0 | 1824 | 0.9928 | 0.88 |
| 0.0002 | 33.0 | 1881 | 1.0014 | 0.88 |
| 0.0115 | 34.0 | 1938 | 0.9989 | 0.88 |
| 0.0001 | 35.0 | 1995 | 0.9871 | 0.88 |
| 0.0001 | 36.0 | 2052 | 0.9920 | 0.88 |
| 0.0002 | 37.0 | 2109 | 0.9974 | 0.88 |
| 0.0002 | 38.0 | 2166 | 0.9950 | 0.88 |
| 0.0001 | 39.0 | 2223 | 0.9997 | 0.88 |
| 0.0001 | 40.0 | 2280 | 0.9951 | 0.88 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
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