divyeshrajpura
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update model card README.md
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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:
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- Accuracy: 0.
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## Model description
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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:
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- eval_batch_size:
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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:
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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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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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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: 0.4795
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- Accuracy: 0.88
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## Model description
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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: 4
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- eval_batch_size: 4
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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: 12
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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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| 2.0422 | 1.0 | 225 | 2.0126 | 0.27 |
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| 1.331 | 2.0 | 450 | 1.3795 | 0.54 |
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| 1.2571 | 3.0 | 675 | 1.0070 | 0.72 |
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| 1.2968 | 4.0 | 900 | 0.8590 | 0.77 |
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| 0.7658 | 5.0 | 1125 | 0.7889 | 0.77 |
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| 0.5499 | 6.0 | 1350 | 0.5743 | 0.82 |
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| 0.8344 | 7.0 | 1575 | 0.6065 | 0.81 |
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| 0.3919 | 8.0 | 1800 | 0.5650 | 0.87 |
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| 0.2808 | 9.0 | 2025 | 0.4605 | 0.87 |
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| 0.4463 | 10.0 | 2250 | 0.5161 | 0.86 |
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| 0.5678 | 11.0 | 2475 | 0.5359 | 0.87 |
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| 0.3032 | 12.0 | 2700 | 0.4795 | 0.88 |
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### Framework versions
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