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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: 0.7931 |
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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: 4e-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: 4 |
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- total_train_batch_size: 16 |
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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: 12 |
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- mixed_precision_training: Native AMP |
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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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| 0.2107 | 1.0 | 56 | 0.4744 | 0.89 | |
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| 0.0867 | 1.99 | 112 | 0.7316 | 0.8 | |
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| 0.1117 | 2.99 | 168 | 0.6942 | 0.81 | |
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| 0.1024 | 4.0 | 225 | 0.6151 | 0.85 | |
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| 0.0141 | 5.0 | 281 | 0.7542 | 0.83 | |
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| 0.0089 | 5.99 | 337 | 0.7236 | 0.85 | |
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| 0.007 | 6.99 | 393 | 0.7115 | 0.84 | |
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| 0.0477 | 8.0 | 450 | 0.7334 | 0.85 | |
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| 0.0048 | 9.0 | 506 | 0.7772 | 0.85 | |
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| 0.0348 | 9.99 | 562 | 0.7465 | 0.85 | |
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| 0.0035 | 10.99 | 618 | 0.8011 | 0.84 | |
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| 0.004 | 11.95 | 672 | 0.7931 | 0.84 | |
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### Framework versions |
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- Transformers 4.36.0.dev0 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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