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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: train
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split: train
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args: train
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.83
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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: 0.6180
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- Accuracy: 0.83
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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: 4
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- eval_batch_size: 4
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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: 7
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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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| 1.6805 | 1.0 | 225 | 1.4984 | 0.64 |
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| 0.8767 | 2.0 | 450 | 1.0174 | 0.67 |
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| 0.9691 | 3.0 | 675 | 0.8688 | 0.73 |
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| 0.6252 | 4.0 | 900 | 0.7695 | 0.77 |
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| 0.2577 | 5.0 | 1125 | 0.6340 | 0.81 |
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| 0.076 | 6.0 | 1350 | 0.6358 | 0.8 |
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| 0.1941 | 7.0 | 1575 | 0.6180 | 0.83 |
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### Framework versions
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- Transformers 4.32.0.dev0
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- Pytorch 2.0.1+cu118
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- Datasets 2.14.1
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- Tokenizers 0.13.3
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