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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: 0.88
---
<!-- 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 [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8080
- 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7263 | 1.0 | 113 | 1.6331 | 0.59 |
| 1.1167 | 2.0 | 226 | 1.2046 | 0.65 |
| 0.774 | 3.0 | 339 | 0.8365 | 0.8 |
| 0.6507 | 4.0 | 452 | 0.7015 | 0.81 |
| 0.5046 | 5.0 | 565 | 0.6722 | 0.81 |
| 0.2632 | 6.0 | 678 | 0.6743 | 0.82 |
| 0.203 | 7.0 | 791 | 0.7351 | 0.84 |
| 0.0902 | 8.0 | 904 | 0.5898 | 0.86 |
| 0.0215 | 9.0 | 1017 | 0.6213 | 0.87 |
| 0.0097 | 10.0 | 1130 | 0.6948 | 0.86 |
| 0.1171 | 11.0 | 1243 | 0.6228 | 0.87 |
| 0.0054 | 12.0 | 1356 | 0.7101 | 0.86 |
| 0.0035 | 13.0 | 1469 | 0.7626 | 0.87 |
| 0.0028 | 14.0 | 1582 | 0.7659 | 0.86 |
| 0.0027 | 15.0 | 1695 | 0.6993 | 0.87 |
| 0.0023 | 16.0 | 1808 | 0.7345 | 0.87 |
| 0.0023 | 17.0 | 1921 | 0.8363 | 0.86 |
| 0.0018 | 18.0 | 2034 | 0.7779 | 0.88 |
| 0.0018 | 19.0 | 2147 | 0.8275 | 0.87 |
| 0.0018 | 20.0 | 2260 | 0.8080 | 0.88 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
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