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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: 0.87
---
<!-- 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: 0.8185
- Accuracy: 0.87
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.668 | 1.0 | 225 | 0.5547 | 0.84 |
| 0.4179 | 2.0 | 450 | 0.7757 | 0.74 |
| 0.0298 | 3.0 | 675 | 0.7077 | 0.84 |
| 0.2144 | 4.0 | 900 | 0.6262 | 0.87 |
| 0.0079 | 5.0 | 1125 | 0.6068 | 0.88 |
| 0.0021 | 6.0 | 1350 | 0.8321 | 0.84 |
| 0.0014 | 7.0 | 1575 | 0.9661 | 0.84 |
| 0.0013 | 8.0 | 1800 | 0.7852 | 0.86 |
| 0.001 | 9.0 | 2025 | 0.8126 | 0.86 |
| 0.001 | 10.0 | 2250 | 0.8185 | 0.87 |
### Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.4.0
- Tokenizers 0.15.0
|