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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: 0.9031
- 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: 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1931 | 1.0 | 113 | 2.0840 | 0.39 |
| 1.5734 | 2.0 | 226 | 1.4764 | 0.53 |
| 1.2619 | 3.0 | 339 | 1.1045 | 0.68 |
| 1.0427 | 4.0 | 452 | 1.0008 | 0.74 |
| 0.7065 | 5.0 | 565 | 0.7131 | 0.83 |
| 0.4206 | 6.0 | 678 | 0.6687 | 0.8 |
| 0.5466 | 7.0 | 791 | 0.5807 | 0.83 |
| 0.1232 | 8.0 | 904 | 0.6143 | 0.83 |
| 0.2593 | 9.0 | 1017 | 0.6080 | 0.89 |
| 0.0496 | 10.0 | 1130 | 0.7360 | 0.84 |
| 0.0127 | 11.0 | 1243 | 0.7648 | 0.85 |
| 0.0993 | 12.0 | 1356 | 0.8416 | 0.85 |
| 0.0068 | 13.0 | 1469 | 0.7966 | 0.85 |
| 0.0054 | 14.0 | 1582 | 0.8122 | 0.86 |
| 0.0044 | 15.0 | 1695 | 0.8788 | 0.87 |
| 0.0037 | 16.0 | 1808 | 0.8760 | 0.87 |
| 0.0892 | 17.0 | 1921 | 0.8911 | 0.87 |
| 0.0032 | 18.0 | 2034 | 0.9083 | 0.86 |
| 0.0029 | 19.0 | 2147 | 0.9172 | 0.86 |
| 0.0037 | 20.0 | 2260 | 0.9031 | 0.87 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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