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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: 0.77 |
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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: 0.8925 |
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- Accuracy: 0.77 |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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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: 15 |
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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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| 2.2921 | 0.98 | 14 | 2.2471 | 0.24 | |
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| 2.1865 | 1.96 | 28 | 2.0565 | 0.45 | |
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| 1.8969 | 2.95 | 42 | 1.7785 | 0.57 | |
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| 1.659 | 4.0 | 57 | 1.5368 | 0.6 | |
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| 1.4989 | 4.98 | 71 | 1.4186 | 0.66 | |
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| 1.3204 | 5.96 | 85 | 1.2775 | 0.68 | |
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| 1.2331 | 6.95 | 99 | 1.2127 | 0.69 | |
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| 1.1486 | 8.0 | 114 | 1.1122 | 0.73 | |
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| 1.0477 | 8.98 | 128 | 1.0672 | 0.73 | |
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| 1.0297 | 9.96 | 142 | 1.0007 | 0.77 | |
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| 0.9469 | 10.95 | 156 | 0.9488 | 0.77 | |
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| 0.8761 | 12.0 | 171 | 0.9259 | 0.77 | |
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| 0.8198 | 12.98 | 185 | 0.9115 | 0.78 | |
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| 0.8503 | 13.96 | 199 | 0.8922 | 0.78 | |
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| 0.8148 | 14.74 | 210 | 0.8925 | 0.77 | |
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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 2.14.0 |
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- Tokenizers 0.13.3 |
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