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--- |
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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-finetuned-gtzan |
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results: [] |
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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-finetuned-gtzan |
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This model is a fine-tuned version of [JanLilan/distilhubert_finetuned-distilhubert](https://huggingface.co/JanLilan/distilhubert_finetuned-distilhubert) on the GTZAN dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6325 |
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- Accuracy: 0.9 |
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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: 0.0005 |
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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: 2 |
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- total_train_batch_size: 16 |
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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: 10 |
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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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| 0.8777 | 0.99 | 33 | 0.4485 | 0.8333 | |
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| 0.6913 | 2.0 | 67 | 1.0592 | 0.7 | |
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| 0.5494 | 2.99 | 100 | 0.6168 | 0.7667 | |
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| 0.3589 | 4.0 | 134 | 0.7820 | 0.7833 | |
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| 0.2049 | 4.99 | 167 | 0.9303 | 0.7833 | |
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| 0.1663 | 6.0 | 201 | 0.3570 | 0.9 | |
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| 0.0446 | 6.99 | 234 | 0.5636 | 0.8667 | |
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| 0.0313 | 8.0 | 268 | 0.6592 | 0.85 | |
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| 0.0007 | 8.99 | 301 | 0.4721 | 0.8833 | |
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| 0.0004 | 9.85 | 330 | 0.6325 | 0.9 | |
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Check it out [colab](https://colab.research.google.com/drive/1hDLWdDKAaULLIkiMNhuz_z5SVGfW-78_?usp=sharing) |
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### Framework versions |
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- Transformers 4.28.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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