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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: 25-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: default |
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split: train[:25%] |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.96 |
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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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# 25-distilhubert-finetuned-gtzan |
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This model is a small fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. |
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It takes 25% of the GTZAN dataset to fine-tune. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1955 |
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- Accuracy: 0.96 |
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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: 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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| 1.3123 | 1.0 | 57 | 1.3463 | 0.56 | |
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| 0.6113 | 2.0 | 114 | 0.5379 | 0.96 | |
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| 0.1295 | 3.0 | 171 | 0.2719 | 0.96 | |
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| 0.2878 | 4.0 | 228 | 0.0979 | 0.96 | |
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| 0.0168 | 5.0 | 285 | 0.1527 | 0.96 | |
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| 0.0104 | 6.0 | 342 | 0.2320 | 0.96 | |
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| 0.0067 | 7.0 | 399 | 0.1798 | 0.96 | |
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| 0.0051 | 8.0 | 456 | 0.1827 | 0.96 | |
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| 0.0041 | 9.0 | 513 | 0.1918 | 0.96 | |
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| 0.0055 | 10.0 | 570 | 0.1955 | 0.96 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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