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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: 0.8
---
<!-- 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: 0.8614
- Accuracy: 0.8
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2268 | 0.99 | 56 | 2.1858 | 0.48 |
| 1.7472 | 2.0 | 113 | 1.6259 | 0.58 |
| 1.3293 | 2.99 | 169 | 1.1815 | 0.72 |
| 1.0368 | 4.0 | 226 | 1.0176 | 0.69 |
| 0.8106 | 4.99 | 282 | 0.8129 | 0.76 |
| 0.5371 | 6.0 | 339 | 0.8296 | 0.72 |
| 0.6545 | 6.99 | 395 | 0.7186 | 0.77 |
| 0.4676 | 8.0 | 452 | 0.6627 | 0.76 |
| 0.2729 | 8.99 | 508 | 0.5993 | 0.84 |
| 0.2113 | 10.0 | 565 | 0.6360 | 0.8 |
| 0.1475 | 10.99 | 621 | 0.6244 | 0.78 |
| 0.0616 | 12.0 | 678 | 0.6762 | 0.83 |
| 0.0429 | 12.99 | 734 | 0.7241 | 0.82 |
| 0.0259 | 14.0 | 791 | 0.7547 | 0.82 |
| 0.0207 | 14.99 | 847 | 0.7636 | 0.82 |
| 0.0179 | 16.0 | 904 | 0.7817 | 0.82 |
| 0.0304 | 16.99 | 960 | 0.7976 | 0.81 |
| 0.0146 | 18.0 | 1017 | 0.8193 | 0.81 |
| 0.0135 | 18.99 | 1073 | 0.8402 | 0.8 |
| 0.0136 | 19.82 | 1120 | 0.8614 | 0.8 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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