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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 |
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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: 0.72 |
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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: 1.3187 |
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- Accuracy: 0.72 |
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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: 2e-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: 4 |
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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: 11 |
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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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| 2.2889 | 0.9912 | 28 | 2.2613 | 0.38 | |
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| 2.1553 | 1.9823 | 56 | 2.0953 | 0.56 | |
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| 1.9626 | 2.9735 | 84 | 1.8820 | 0.54 | |
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| 1.7839 | 4.0 | 113 | 1.7308 | 0.61 | |
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| 1.6749 | 4.9912 | 141 | 1.5920 | 0.64 | |
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| 1.5595 | 5.9823 | 169 | 1.5004 | 0.68 | |
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| 1.5266 | 6.9735 | 197 | 1.4368 | 0.68 | |
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| 1.4459 | 8.0 | 226 | 1.3776 | 0.71 | |
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| 1.4152 | 8.9912 | 254 | 1.3481 | 0.71 | |
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| 1.3766 | 9.9823 | 282 | 1.3242 | 0.72 | |
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| 1.3682 | 10.9027 | 308 | 1.3187 | 0.72 | |
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### Framework versions |
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- Transformers 4.40.0 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
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