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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: AudioCourseU4-MusicClassification
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. -->
# AudioCourseU4-MusicClassification
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: 0.8804
- 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: 8e-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: 0.1
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7993 | 1.0 | 225 | 1.5770 | 0.4 |
| 1.0767 | 2.0 | 450 | 0.9900 | 0.7 |
| 0.8292 | 3.0 | 675 | 0.8554 | 0.73 |
| 0.5892 | 4.0 | 900 | 0.8991 | 0.74 |
| 0.1584 | 5.0 | 1125 | 0.8473 | 0.78 |
| 0.0082 | 6.0 | 1350 | 0.9282 | 0.8 |
| 0.0094 | 7.0 | 1575 | 1.0036 | 0.82 |
| 0.0581 | 8.0 | 1800 | 1.2186 | 0.82 |
| 0.0021 | 9.0 | 2025 | 1.0192 | 0.83 |
| 0.0011 | 10.0 | 2250 | 0.8804 | 0.88 |
| 0.002 | 11.0 | 2475 | 1.1519 | 0.83 |
| 0.0009 | 12.0 | 2700 | 0.9439 | 0.87 |
| 0.0006 | 13.0 | 2925 | 1.1227 | 0.84 |
| 0.0008 | 14.0 | 3150 | 1.0344 | 0.86 |
| 0.0006 | 15.0 | 3375 | 1.0209 | 0.86 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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