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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: ntu-spml/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. -->
# ntu-spml/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: 0.7428
- 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: 3.992986714871485e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9807885777224674,0.996064720140604) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 509
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 57 | 2.2832 | 0.3 |
| No log | 2.0 | 114 | 2.2273 | 0.28 |
| No log | 3.0 | 171 | 2.0861 | 0.46 |
| No log | 4.0 | 228 | 1.8473 | 0.5 |
| No log | 5.0 | 285 | 1.5146 | 0.6 |
| No log | 6.0 | 342 | 1.2140 | 0.69 |
| No log | 7.0 | 399 | 0.9856 | 0.74 |
| No log | 8.0 | 456 | 0.8056 | 0.79 |
| 1.6591 | 9.0 | 513 | 0.7135 | 0.8 |
| 1.6591 | 10.0 | 570 | 0.7642 | 0.75 |
| 1.6591 | 11.0 | 627 | 0.6344 | 0.79 |
| 1.6591 | 12.0 | 684 | 0.5982 | 0.83 |
| 1.6591 | 13.0 | 741 | 0.5369 | 0.86 |
| 1.6591 | 14.0 | 798 | 0.7501 | 0.79 |
| 1.6591 | 15.0 | 855 | 0.7493 | 0.78 |
| 1.6591 | 16.0 | 912 | 0.6891 | 0.83 |
| 1.6591 | 17.0 | 969 | 0.7492 | 0.8 |
| 0.2402 | 18.0 | 1026 | 0.6663 | 0.88 |
| 0.2402 | 19.0 | 1083 | 0.5750 | 0.89 |
| 0.2402 | 20.0 | 1140 | 0.8215 | 0.81 |
| 0.2402 | 21.0 | 1197 | 0.7435 | 0.79 |
| 0.2402 | 22.0 | 1254 | 0.8305 | 0.86 |
| 0.2402 | 23.0 | 1311 | 0.7636 | 0.83 |
| 0.2402 | 24.0 | 1368 | 0.9786 | 0.77 |
| 0.2402 | 25.0 | 1425 | 0.7082 | 0.88 |
| 0.2402 | 26.0 | 1482 | 0.7698 | 0.85 |
| 0.0206 | 27.0 | 1539 | 0.7360 | 0.87 |
| 0.0206 | 28.0 | 1596 | 0.8575 | 0.84 |
| 0.0206 | 29.0 | 1653 | 0.7428 | 0.87 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1
- Datasets 2.13.2.dev1
- Tokenizers 0.13.3
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