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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-gtzanVD
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9839786381842457
---
<!-- 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-gtzanVD
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: 0.1334
- Accuracy: 0.9840
## 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
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3554 | 1.0 | 842 | 0.1898 | 0.9439 |
| 0.1136 | 2.0 | 1684 | 0.1657 | 0.9626 |
| 0.1571 | 3.0 | 2526 | 0.1132 | 0.9693 |
| 0.0004 | 4.0 | 3368 | 0.1235 | 0.9786 |
| 0.0011 | 5.0 | 4210 | 0.1555 | 0.9680 |
| 0.0001 | 6.0 | 5052 | 0.3138 | 0.9493 |
| 0.0001 | 7.0 | 5894 | 0.1825 | 0.9680 |
| 0.0001 | 8.0 | 6736 | 0.1982 | 0.9706 |
| 0.0001 | 9.0 | 7578 | 0.1690 | 0.9693 |
| 0.3166 | 10.0 | 8420 | 0.1487 | 0.9733 |
| 0.0 | 11.0 | 9262 | 0.2615 | 0.9680 |
| 0.0 | 12.0 | 10104 | 0.1536 | 0.9800 |
| 0.0001 | 13.0 | 10946 | 0.5478 | 0.9399 |
| 0.0 | 14.0 | 11788 | 0.1334 | 0.9840 |
| 0.0 | 15.0 | 12630 | 0.1270 | 0.9746 |
| 0.0 | 16.0 | 13472 | 0.1053 | 0.9840 |
| 0.0 | 17.0 | 14314 | 0.1181 | 0.9813 |
| 0.0 | 18.0 | 15156 | 0.1165 | 0.9826 |
| 0.0 | 19.0 | 15998 | 0.1191 | 0.9826 |
| 0.0 | 20.0 | 16840 | 0.1188 | 0.9826 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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