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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: 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: 0.8581829692940804 |
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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: 1.5627 |
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- Accuracy: 0.8582 |
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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: 8 |
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- eval_batch_size: 8 |
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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: 20 |
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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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| 0.4173 | 1.0 | 7108 | 0.5416 | 0.8343 | |
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| 0.235 | 2.0 | 14216 | 0.4663 | 0.8251 | |
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| 0.1549 | 3.0 | 21324 | 0.5940 | 0.8325 | |
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| 0.2558 | 4.0 | 28432 | 0.6608 | 0.8531 | |
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| 0.2991 | 5.0 | 35540 | 0.9088 | 0.8305 | |
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| 0.4773 | 6.0 | 42648 | 0.9120 | 0.8390 | |
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| 0.5235 | 7.0 | 49756 | 0.9285 | 0.8455 | |
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| 0.0004 | 8.0 | 56864 | 1.0259 | 0.8492 | |
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| 0.1918 | 9.0 | 63972 | 1.2874 | 0.8411 | |
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| 0.0002 | 10.0 | 71080 | 1.1114 | 0.8476 | |
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| 0.0001 | 11.0 | 78188 | 1.4835 | 0.8393 | |
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| 0.0013 | 12.0 | 85296 | 1.3846 | 0.8541 | |
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| 0.0001 | 13.0 | 92404 | 1.3622 | 0.8507 | |
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| 0.0909 | 14.0 | 99512 | 1.4672 | 0.8487 | |
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| 0.0001 | 15.0 | 106620 | 1.4243 | 0.8571 | |
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| 0.0 | 16.0 | 113728 | 1.5627 | 0.8582 | |
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| 0.0 | 17.0 | 120836 | 1.8146 | 0.8531 | |
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| 0.0 | 18.0 | 127944 | 1.8596 | 0.8550 | |
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| 0.0 | 19.0 | 135052 | 1.9233 | 0.8574 | |
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| 0.0 | 20.0 | 142160 | 1.9875 | 0.8569 | |
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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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