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---
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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-v3
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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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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.87
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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-v3
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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: 1.0906
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- Accuracy: 0.87
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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: 1
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- eval_batch_size: 1
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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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- 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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| 0.8287 | 1.0 | 899 | 0.8991 | 0.68 |
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| 2.2164 | 2.0 | 1798 | 1.3184 | 0.71 |
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| 0.0099 | 3.0 | 2697 | 0.9288 | 0.78 |
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| 0.0679 | 4.0 | 3596 | 0.8131 | 0.84 |
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| 0.0119 | 5.0 | 4495 | 1.1122 | 0.8 |
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| 0.0051 | 6.0 | 5394 | 0.9594 | 0.86 |
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| 0.0008 | 7.0 | 6293 | 0.9475 | 0.87 |
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| 0.0002 | 8.0 | 7192 | 1.1026 | 0.86 |
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| 0.0002 | 9.0 | 8091 | 1.0751 | 0.87 |
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| 0.0002 | 10.0 | 8990 | 1.0906 | 0.87 |
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### Framework versions
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- Transformers 4.44.0
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- Pytorch 2.1.1+cu118
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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