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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: 0.81
---
<!-- 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: 1.0178
- Accuracy: 0.81
## 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: 16
- eval_batch_size: 16
- 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: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2598 | 1.0 | 57 | 2.2140 | 0.34 |
| 1.8981 | 2.0 | 114 | 1.8262 | 0.56 |
| 1.4487 | 3.0 | 171 | 1.4402 | 0.64 |
| 1.1792 | 4.0 | 228 | 1.1520 | 0.69 |
| 0.9231 | 5.0 | 285 | 0.9415 | 0.75 |
| 0.7141 | 6.0 | 342 | 0.8904 | 0.73 |
| 0.5477 | 7.0 | 399 | 0.7395 | 0.78 |
| 0.3968 | 8.0 | 456 | 0.6359 | 0.81 |
| 0.4259 | 9.0 | 513 | 0.6345 | 0.8 |
| 0.2474 | 10.0 | 570 | 0.6333 | 0.8 |
| 0.1379 | 11.0 | 627 | 0.5374 | 0.83 |
| 0.0781 | 12.0 | 684 | 0.6484 | 0.84 |
| 0.0337 | 13.0 | 741 | 0.7072 | 0.84 |
| 0.0211 | 14.0 | 798 | 0.7023 | 0.83 |
| 0.0135 | 15.0 | 855 | 0.8199 | 0.83 |
| 0.0097 | 16.0 | 912 | 0.8009 | 0.83 |
| 0.065 | 17.0 | 969 | 0.8992 | 0.81 |
| 0.0067 | 18.0 | 1026 | 0.8628 | 0.82 |
| 0.0118 | 19.0 | 1083 | 0.6922 | 0.85 |
| 0.0052 | 20.0 | 1140 | 0.8001 | 0.84 |
| 0.077 | 21.0 | 1197 | 0.8324 | 0.82 |
| 0.0043 | 22.0 | 1254 | 0.9468 | 0.8 |
| 0.0039 | 23.0 | 1311 | 0.8866 | 0.8 |
| 0.0696 | 24.0 | 1368 | 0.9424 | 0.82 |
| 0.0037 | 25.0 | 1425 | 0.7855 | 0.81 |
| 0.0631 | 26.0 | 1482 | 0.7659 | 0.82 |
| 0.0592 | 27.0 | 1539 | 0.8605 | 0.83 |
| 0.0034 | 28.0 | 1596 | 0.9266 | 0.82 |
| 0.0032 | 29.0 | 1653 | 0.9831 | 0.82 |
| 0.0032 | 30.0 | 1710 | 1.0178 | 0.81 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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