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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: 0.84
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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: 0.5613
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- Accuracy: 0.84
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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: 8
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- eval_batch_size: 8
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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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| 2.0005 | 1.0 | 113 | 1.8064 | 0.56 |
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| 1.2982 | 2.0 | 226 | 1.1525 | 0.64 |
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| 0.9748 | 3.0 | 339 | 0.9099 | 0.77 |
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| 0.7589 | 4.0 | 452 | 0.7924 | 0.77 |
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| 0.68 | 5.0 | 565 | 0.6832 | 0.82 |
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| 0.3658 | 6.0 | 678 | 0.6149 | 0.81 |
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| 0.3897 | 7.0 | 791 | 0.5371 | 0.85 |
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| 0.1689 | 8.0 | 904 | 0.5549 | 0.85 |
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| 0.2707 | 9.0 | 1017 | 0.5365 | 0.84 |
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| 0.1762 | 10.0 | 1130 | 0.5613 | 0.84 |
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### Framework versions
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- Transformers 4.31.0
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- Pytorch 2.0.1+cu118
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- Datasets 2.14.4
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- Tokenizers 0.13.3
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