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update model card README.md
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README.md
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---
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license: apache-2.0
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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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---
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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: 0.6228
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- Accuracy: 0.85
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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: 4e-05
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- train_batch_size: 16
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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: 32
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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: 20
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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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| 2.2841 | 0.98 | 28 | 2.2578 | 0.22 |
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| 2.108 | 2.0 | 57 | 2.0031 | 0.55 |
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| 1.7117 | 2.98 | 85 | 1.6220 | 0.65 |
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| 1.4624 | 4.0 | 114 | 1.4061 | 0.7 |
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| 1.2607 | 4.98 | 142 | 1.1969 | 0.69 |
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| 1.1162 | 6.0 | 171 | 1.0955 | 0.75 |
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| 1.0 | 6.98 | 199 | 0.9670 | 0.78 |
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| 0.8864 | 8.0 | 228 | 0.9192 | 0.77 |
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| 0.8583 | 8.98 | 256 | 0.8475 | 0.78 |
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| 0.8147 | 10.0 | 285 | 0.8214 | 0.77 |
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| 0.6572 | 10.98 | 313 | 0.7754 | 0.78 |
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| 0.5958 | 12.0 | 342 | 0.7187 | 0.79 |
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| 0.4196 | 12.98 | 370 | 0.6732 | 0.83 |
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| 0.4515 | 14.0 | 399 | 0.7272 | 0.8 |
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| 0.4256 | 14.98 | 427 | 0.6507 | 0.84 |
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| 0.3734 | 16.0 | 456 | 0.6587 | 0.83 |
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| 0.3541 | 16.98 | 484 | 0.6244 | 0.86 |
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| 0.312 | 18.0 | 513 | 0.6363 | 0.84 |
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| 0.3287 | 18.98 | 541 | 0.6226 | 0.86 |
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| 0.313 | 19.65 | 560 | 0.6228 | 0.85 |
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### Framework versions
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- Transformers 4.30.2
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- Pytorch 2.0.1+cu118
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- Datasets 2.13.1
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- Tokenizers 0.13.3
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