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---
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library_name: transformers
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license: apache-2.0
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base_model: ntu-spml/distilhubert
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tags:
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- generated_from_trainer
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datasets:
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- marsyas/gtzan
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metrics:
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- accuracy
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model-index:
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- name: distilhubert-finetuned-gtzan
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results:
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- task:
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name: Audio Classification
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type: audio-classification
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dataset:
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name: GTZAN
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type: marsyas/gtzan
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config: all
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split: train
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args: all
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.7435897435897436
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# distilhubert-finetuned-gtzan
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This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.9861
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- Accuracy: 0.7436
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size: 10
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- eval_batch_size: 10
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 20
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 2.1171 | 1.0 | 70 | 2.1232 | 0.2308 |
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| 1.534 | 2.0 | 140 | 1.6014 | 0.5128 |
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| 1.4328 | 3.0 | 210 | 1.2896 | 0.5641 |
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| 0.8631 | 4.0 | 280 | 1.1275 | 0.5897 |
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| 0.6448 | 5.0 | 350 | 1.0679 | 0.6667 |
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| 0.482 | 6.0 | 420 | 0.8798 | 0.7051 |
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| 0.2458 | 7.0 | 490 | 0.8290 | 0.7564 |
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| 0.2264 | 8.0 | 560 | 0.8350 | 0.7564 |
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| 0.1661 | 9.0 | 630 | 0.8284 | 0.7179 |
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| 0.0286 | 10.0 | 700 | 0.9681 | 0.7179 |
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| 0.0155 | 11.0 | 770 | 0.9861 | 0.7436 |
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### Framework versions
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- Transformers 4.45.1
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- Pytorch 2.4.1+cu121
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- Datasets 3.0.1
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- Tokenizers 0.20.0
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