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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: 0.78
---
<!-- 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: 1.1493
- Accuracy: 0.78
## 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: 0.00018
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7553 | 1.0 | 113 | 1.5918 | 0.47 |
| 1.0465 | 2.0 | 226 | 0.9806 | 0.68 |
| 1.2995 | 3.0 | 339 | 0.9627 | 0.72 |
| 1.1928 | 4.0 | 452 | 0.9208 | 0.71 |
| 0.3715 | 5.0 | 565 | 0.5924 | 0.81 |
| 0.4474 | 6.0 | 678 | 1.0245 | 0.71 |
| 0.4553 | 7.0 | 791 | 0.8025 | 0.79 |
| 0.029 | 8.0 | 904 | 1.3956 | 0.71 |
| 0.2059 | 9.0 | 1017 | 1.1544 | 0.79 |
| 0.1797 | 10.0 | 1130 | 1.6616 | 0.74 |
| 0.0009 | 11.0 | 1243 | 0.9263 | 0.86 |
| 0.1761 | 12.0 | 1356 | 0.9989 | 0.85 |
| 0.0006 | 13.0 | 1469 | 1.2108 | 0.8 |
| 0.0006 | 14.0 | 1582 | 0.9643 | 0.83 |
| 0.0005 | 15.0 | 1695 | 1.1004 | 0.8 |
| 0.0004 | 16.0 | 1808 | 1.0556 | 0.82 |
| 0.1084 | 17.0 | 1921 | 1.1447 | 0.81 |
| 0.0003 | 18.0 | 2034 | 1.1467 | 0.82 |
| 0.0003 | 19.0 | 2147 | 1.1723 | 0.8 |
| 0.0003 | 20.0 | 2260 | 1.1493 | 0.78 |
### Framework versions
- Transformers 4.32.1
- Pytorch 1.13.1
- Datasets 2.14.4
- Tokenizers 0.13.3
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