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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: 0.9492
- 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: 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1278 | 1.0 | 113 | 1.9945 | 0.46 |
| 1.422 | 2.0 | 226 | 1.3210 | 0.63 |
| 1.0769 | 3.0 | 339 | 0.9838 | 0.77 |
| 0.8781 | 4.0 | 452 | 0.8076 | 0.75 |
| 0.6584 | 5.0 | 565 | 0.6962 | 0.79 |
| 0.4766 | 6.0 | 678 | 0.5555 | 0.84 |
| 0.3916 | 7.0 | 791 | 0.5909 | 0.84 |
| 0.1187 | 8.0 | 904 | 0.6129 | 0.81 |
| 0.1442 | 9.0 | 1017 | 0.7126 | 0.79 |
| 0.1238 | 10.0 | 1130 | 0.8089 | 0.8 |
| 0.0291 | 11.0 | 1243 | 0.8908 | 0.79 |
| 0.0821 | 12.0 | 1356 | 0.8962 | 0.81 |
| 0.0104 | 13.0 | 1469 | 0.8957 | 0.81 |
| 0.0311 | 14.0 | 1582 | 0.9264 | 0.81 |
| 0.0107 | 15.0 | 1695 | 0.9492 | 0.81 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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