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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: 0.85 |
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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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- Accuracy: 0.85 |
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- Loss: 0.7531 |
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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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- gradient_accumulation_steps: 16 |
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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: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 20 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Accuracy | Validation Loss | |
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|:-------------:|:-----:|:----:|:--------:|:---------------:| |
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| 2.2849 | 1.0 | 14 | 0.17 | 2.2588 | |
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| 2.1931 | 1.99 | 28 | 0.47 | 2.0874 | |
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| 1.9194 | 2.99 | 42 | 0.58 | 1.8044 | |
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| 1.6351 | 3.98 | 56 | 0.61 | 1.5806 | |
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| 1.4473 | 4.98 | 70 | 0.71 | 1.3886 | |
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| 1.3131 | 5.97 | 84 | 0.7 | 1.2738 | |
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| 1.2141 | 6.97 | 98 | 0.72 | 1.1616 | |
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| 1.0657 | 7.96 | 112 | 0.74 | 1.1272 | |
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| 0.96 | 8.96 | 126 | 0.75 | 1.0251 | |
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| 0.8387 | 9.96 | 140 | 0.8 | 0.9364 | |
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| 0.8653 | 10.95 | 154 | 0.79 | 0.8858 | |
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| 0.7653 | 11.95 | 168 | 0.8 | 0.8233 | |
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| 0.7329 | 12.94 | 182 | 0.83 | 0.7982 | |
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| 0.675 | 13.94 | 196 | 0.81 | 0.8189 | |
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| 0.6174 | 14.93 | 210 | 0.82 | 0.8236 | |
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| 0.5714 | 16.0 | 225 | 0.82 | 0.7755 | |
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| 0.598 | 17.0 | 239 | 0.81 | 0.7511 | |
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| 0.5794 | 17.99 | 253 | 0.84 | 0.7553 | |
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| 0.589 | 18.99 | 267 | 0.85 | 0.7533 | |
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| 0.5717 | 19.91 | 280 | 0.85 | 0.7531 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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