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README.md
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.
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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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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.
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- Accuracy: 0.
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## Model description
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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:
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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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### Framework versions
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- Transformers 4.
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- Pytorch 2.0.1+cu118
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- Datasets 2.
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- Tokenizers 0.13.3
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.81
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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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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: 0.9492
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- Accuracy: 0.81
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## Model description
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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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| 2.1278 | 1.0 | 113 | 1.9945 | 0.46 |
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| 1.422 | 2.0 | 226 | 1.3210 | 0.63 |
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| 1.0769 | 3.0 | 339 | 0.9838 | 0.77 |
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| 0.8781 | 4.0 | 452 | 0.8076 | 0.75 |
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| 0.6584 | 5.0 | 565 | 0.6962 | 0.79 |
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| 0.4766 | 6.0 | 678 | 0.5555 | 0.84 |
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| 0.3916 | 7.0 | 791 | 0.5909 | 0.84 |
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| 0.1187 | 8.0 | 904 | 0.6129 | 0.81 |
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| 0.1442 | 9.0 | 1017 | 0.7126 | 0.79 |
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| 0.1238 | 10.0 | 1130 | 0.8089 | 0.8 |
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| 0.0291 | 11.0 | 1243 | 0.8908 | 0.79 |
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| 0.0821 | 12.0 | 1356 | 0.8962 | 0.81 |
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| 0.0104 | 13.0 | 1469 | 0.8957 | 0.81 |
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| 0.0311 | 14.0 | 1582 | 0.9264 | 0.81 |
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| 0.0107 | 15.0 | 1695 | 0.9492 | 0.81 |
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### Framework versions
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- Transformers 4.31.0
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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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