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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: facebook/hubert-base-ls960 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- gtzan |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: hubert-model-v2 |
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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: gtzan |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.82 |
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- name: Precision |
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type: precision |
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value: 0.8391562316626255 |
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- name: Recall |
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type: recall |
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value: 0.82 |
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- name: F1 |
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type: f1 |
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value: 0.822405644289722 |
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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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# hubert-model-v2 |
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This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the gtzan dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9612 |
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- Accuracy: 0.82 |
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- Precision: 0.8392 |
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- Recall: 0.82 |
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- F1: 0.8224 |
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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: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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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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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 2.2086 | 1.0 | 200 | 2.0834 | 0.265 | 0.1987 | 0.265 | 0.1723 | |
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| 1.8657 | 2.0 | 400 | 1.7149 | 0.385 | 0.3228 | 0.385 | 0.3226 | |
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| 1.5242 | 3.0 | 600 | 1.4365 | 0.49 | 0.4463 | 0.49 | 0.4280 | |
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| 1.2542 | 4.0 | 800 | 1.2907 | 0.57 | 0.6029 | 0.57 | 0.5477 | |
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| 1.0119 | 5.0 | 1000 | 1.0524 | 0.69 | 0.7249 | 0.69 | 0.6745 | |
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| 0.8835 | 6.0 | 1200 | 1.1677 | 0.69 | 0.7156 | 0.69 | 0.6718 | |
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| 0.7271 | 7.0 | 1400 | 0.9158 | 0.745 | 0.7569 | 0.745 | 0.7309 | |
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| 0.5453 | 8.0 | 1600 | 0.7592 | 0.82 | 0.8296 | 0.82 | 0.8185 | |
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| 0.4503 | 9.0 | 1800 | 0.9936 | 0.775 | 0.8138 | 0.775 | 0.7799 | |
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| 0.3625 | 10.0 | 2000 | 0.9192 | 0.815 | 0.8258 | 0.815 | 0.8146 | |
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| 0.2773 | 11.0 | 2200 | 0.9768 | 0.82 | 0.8362 | 0.82 | 0.8201 | |
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| 0.2309 | 12.0 | 2400 | 0.9612 | 0.82 | 0.8392 | 0.82 | 0.8224 | |
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### Framework versions |
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- Transformers 4.47.1 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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