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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: 0.86 |
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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: 0.7337 |
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- Accuracy: 0.86 |
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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: 6e-05 |
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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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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 12 |
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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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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 2.1397 | 1.0 | 75 | 2.0011 | 0.45 | |
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| 1.4889 | 2.0 | 150 | 1.3599 | 0.66 | |
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| 1.0109 | 3.0 | 225 | 1.0052 | 0.74 | |
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| 0.7499 | 4.0 | 300 | 0.8884 | 0.77 | |
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| 0.5627 | 5.0 | 375 | 0.6333 | 0.86 | |
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| 0.4138 | 6.0 | 450 | 0.5492 | 0.81 | |
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| 0.2909 | 7.0 | 525 | 0.6417 | 0.81 | |
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| 0.1475 | 8.0 | 600 | 0.5900 | 0.84 | |
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| 0.0845 | 9.0 | 675 | 0.6959 | 0.84 | |
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| 0.0619 | 10.0 | 750 | 0.6587 | 0.86 | |
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| 0.0233 | 11.0 | 825 | 0.7675 | 0.82 | |
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| 0.0168 | 12.0 | 900 | 0.7352 | 0.83 | |
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| 0.0152 | 13.0 | 975 | 0.7293 | 0.87 | |
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| 0.0136 | 14.0 | 1050 | 0.7490 | 0.86 | |
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| 0.0123 | 15.0 | 1125 | 0.7337 | 0.86 | |
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### Framework versions |
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- Transformers 4.32.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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