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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-VD |
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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 |
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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: 0.8933256172839507 |
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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-VD |
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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: 0.7226 |
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- Accuracy: 0.8933 |
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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: 10 |
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- eval_batch_size: 10 |
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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: 25 |
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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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| 0.3302 | 1.0 | 195 | 0.3716 | 0.8800 | |
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| 0.6059 | 2.0 | 390 | 0.5195 | 0.8090 | |
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| 0.4938 | 3.0 | 585 | 1.0102 | 0.6260 | |
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| 0.836 | 4.0 | 780 | 1.1662 | 0.6742 | |
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| 0.2234 | 5.0 | 975 | 0.6792 | 0.8389 | |
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| 0.1444 | 6.0 | 1170 | 0.9137 | 0.8239 | |
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| 0.2986 | 7.0 | 1365 | 0.7987 | 0.8623 | |
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| 0.0004 | 8.0 | 1560 | 1.5075 | 0.7687 | |
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| 0.0005 | 9.0 | 1755 | 0.7226 | 0.8933 | |
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| 0.0002 | 10.0 | 1950 | 0.8246 | 0.8829 | |
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| 0.0002 | 11.0 | 2145 | 1.4227 | 0.8129 | |
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| 0.0001 | 12.0 | 2340 | 1.0478 | 0.8665 | |
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| 0.0001 | 13.0 | 2535 | 1.3328 | 0.8322 | |
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| 0.0001 | 14.0 | 2730 | 1.3480 | 0.8347 | |
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| 0.0001 | 15.0 | 2925 | 1.3559 | 0.8370 | |
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| 0.0 | 16.0 | 3120 | 1.3589 | 0.8407 | |
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| 0.0 | 17.0 | 3315 | 1.3706 | 0.8410 | |
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| 0.0 | 18.0 | 3510 | 1.3831 | 0.8410 | |
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| 0.0 | 19.0 | 3705 | 1.3954 | 0.8410 | |
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| 0.0 | 20.0 | 3900 | 1.4027 | 0.8412 | |
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| 0.0 | 21.0 | 4095 | 1.4132 | 0.8409 | |
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| 0.0 | 22.0 | 4290 | 1.4218 | 0.8407 | |
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| 0.0 | 23.0 | 4485 | 1.4272 | 0.8407 | |
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| 0.0 | 24.0 | 4680 | 1.4321 | 0.8399 | |
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| 0.0 | 25.0 | 4875 | 1.4337 | 0.8399 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.2.2+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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