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README.md
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---
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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: 0.82
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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: 0.5914
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- Accuracy: 0.82
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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: 0.0001
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- train_batch_size: 16
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- eval_batch_size: 16
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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: 10
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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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| 1.9157 | 1.0 | 57 | 1.7318 | 0.45 |
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| 1.2425 | 2.0 | 114 | 1.1454 | 0.7 |
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| 0.8878 | 3.0 | 171 | 0.8611 | 0.76 |
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| 0.6978 | 4.0 | 228 | 0.7691 | 0.77 |
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| 0.4136 | 5.0 | 285 | 0.6523 | 0.82 |
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| 0.2548 | 6.0 | 342 | 0.5780 | 0.84 |
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| 0.183 | 7.0 | 399 | 0.6210 | 0.81 |
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| 0.0801 | 8.0 | 456 | 0.6295 | 0.82 |
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| 0.0709 | 9.0 | 513 | 0.6664 | 0.82 |
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| 0.0423 | 10.0 | 570 | 0.5914 | 0.82 |
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### Framework versions
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- Transformers 4.31.0
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- Pytorch 1.13.1
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- Datasets 2.13.1
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- Tokenizers 0.13.3
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