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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-gtzan2 |
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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: 0.7125 |
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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-gtzan2 |
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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: 1.5220 |
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- Accuracy: 0.7125 |
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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: 32 |
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- eval_batch_size: 32 |
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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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| 1.7489 | 1.0 | 29 | 1.4959 | 0.3875 | |
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| 1.328 | 2.0 | 58 | 2.0243 | 0.35 | |
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| 1.2168 | 3.0 | 87 | 1.1332 | 0.5875 | |
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| 1.0299 | 4.0 | 116 | 1.4826 | 0.5375 | |
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| 0.911 | 5.0 | 145 | 1.2510 | 0.625 | |
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| 1.0819 | 6.0 | 174 | 1.7365 | 0.55 | |
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| 0.9513 | 7.0 | 203 | 1.3000 | 0.6 | |
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| 0.5687 | 8.0 | 232 | 1.0503 | 0.7125 | |
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| 0.4684 | 9.0 | 261 | 1.1167 | 0.7125 | |
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| 0.2836 | 10.0 | 290 | 1.5990 | 0.65 | |
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| 0.138 | 11.0 | 319 | 1.2096 | 0.7375 | |
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| 0.0406 | 12.0 | 348 | 1.7311 | 0.6375 | |
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| 0.0341 | 13.0 | 377 | 1.7048 | 0.6375 | |
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| 0.0059 | 14.0 | 406 | 1.4933 | 0.7 | |
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| 0.0034 | 15.0 | 435 | 1.5220 | 0.7125 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.17.0 |
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- Tokenizers 0.15.2 |
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