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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: 0.88 |
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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: 0.88 |
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- Loss: 0.4331 |
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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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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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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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| 2.2693 | 0.99 | 28 | 0.31 | 2.2480 | |
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| 1.9782 | 1.98 | 56 | 0.45 | 1.8990 | |
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| 1.6438 | 2.97 | 84 | 0.62 | 1.5180 | |
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| 1.3307 | 4.0 | 113 | 0.73 | 1.2206 | |
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| 1.133 | 4.99 | 141 | 0.76 | 0.9961 | |
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| 0.9384 | 5.98 | 169 | 0.78 | 0.8889 | |
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| 0.8668 | 6.97 | 197 | 0.79 | 0.7543 | |
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| 0.674 | 8.0 | 226 | 0.79 | 0.7433 | |
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| 0.5997 | 8.99 | 254 | 0.83 | 0.6194 | |
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| 0.5195 | 9.98 | 282 | 0.91 | 0.5685 | |
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| 0.401 | 10.97 | 310 | 0.91 | 0.5144 | |
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| 0.3151 | 12.0 | 339 | 0.87 | 0.4775 | |
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| 0.2653 | 12.99 | 367 | 0.88 | 0.4984 | |
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| 0.2182 | 13.98 | 395 | 0.88 | 0.4337 | |
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| 0.2036 | 14.97 | 423 | 0.89 | 0.4657 | |
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| 0.1925 | 16.0 | 452 | 0.89 | 0.4222 | |
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| 0.1807 | 16.99 | 480 | 0.87 | 0.4512 | |
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| 0.1626 | 17.98 | 508 | 0.88 | 0.4247 | |
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| 0.1388 | 18.97 | 536 | 0.88 | 0.4324 | |
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| 0.1718 | 19.82 | 560 | 0.88 | 0.4331 | |
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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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