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--- |
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license: apache-2.0 |
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library_name: peft |
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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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base_model: ntu-spml/distilhubert |
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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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type: audio-classification |
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name: Audio Classification |
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dataset: |
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name: GTZAN |
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type: marsyas/gtzan |
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split: None |
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metrics: |
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- type: accuracy |
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value: 0.9319319319319319 |
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name: Accuracy |
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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: 0.2387 |
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- Accuracy: 0.9319 |
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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: 0.001 |
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- train_batch_size: 6 |
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- eval_batch_size: 6 |
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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: 15 |
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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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| 1.7644 | 1.0 | 167 | 1.7832 | 0.3554 | |
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| 1.2856 | 2.0 | 334 | 1.4226 | 0.4745 | |
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| 1.2123 | 3.0 | 501 | 1.0047 | 0.6737 | |
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| 0.6613 | 4.0 | 668 | 0.8091 | 0.6987 | |
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| 0.6442 | 5.0 | 835 | 0.6713 | 0.7858 | |
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| 0.7172 | 6.0 | 1002 | 0.5749 | 0.8238 | |
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| 0.5394 | 7.0 | 1169 | 0.5079 | 0.8408 | |
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| 0.3853 | 8.0 | 1336 | 0.4574 | 0.8539 | |
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| 0.5441 | 9.0 | 1503 | 0.3729 | 0.8869 | |
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| 0.5062 | 10.0 | 1670 | 0.3319 | 0.9009 | |
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| 0.3955 | 11.0 | 1837 | 0.3745 | 0.8849 | |
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| 0.3112 | 12.0 | 2004 | 0.2752 | 0.9289 | |
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| 0.2887 | 13.0 | 2171 | 0.2544 | 0.9289 | |
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| 0.2038 | 14.0 | 2338 | 0.2344 | 0.9329 | |
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| 0.2374 | 15.0 | 2505 | 0.2387 | 0.9319 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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## Training procedure |
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### Framework versions |
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- PEFT 0.6.2 |
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