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---
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library_name: transformers
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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: 0.7435897435897436
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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: 0.8173
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- Accuracy: 0.7436
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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: 5e-05
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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: 20
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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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| 2.1874 | 1.0 | 44 | 2.1429 | 0.3974 |
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| 1.8257 | 2.0 | 88 | 1.7390 | 0.4872 |
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| 1.4881 | 3.0 | 132 | 1.3711 | 0.6026 |
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| 1.0373 | 4.0 | 176 | 1.1632 | 0.6667 |
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| 0.7621 | 5.0 | 220 | 1.0026 | 0.7308 |
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| 0.6114 | 6.0 | 264 | 0.8857 | 0.7436 |
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| 0.5642 | 7.0 | 308 | 0.8796 | 0.7179 |
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| 0.3386 | 8.0 | 352 | 1.0714 | 0.6923 |
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| 0.3364 | 9.0 | 396 | 0.8363 | 0.7308 |
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| 0.1678 | 10.0 | 440 | 0.7834 | 0.7436 |
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| 0.1154 | 11.0 | 484 | 0.8173 | 0.7436 |
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### Framework versions
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- Transformers 4.45.1
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- Pytorch 2.4.1+cu121
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- Datasets 3.0.1
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- Tokenizers 0.20.0
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