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--- |
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license: bsd-3-clause |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: ast_18-finetuned-ICBHI |
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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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# ast_18-finetuned-ICBHI |
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This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0867 |
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- Accuracy: 0.5757 |
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- Sensitivity: 0.1164 |
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- Specificity: 0.9183 |
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- Score: 0.5173 |
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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: 3e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Sensitivity | Specificity | Score | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:-----------:|:------:| |
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| 1.5436 | 0.98 | 32 | 1.4656 | 0.1684 | 0.3127 | 0.0608 | 0.1867 | |
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| 1.2922 | 2.0 | 65 | 1.2055 | 0.4530 | 0.1121 | 0.7072 | 0.4097 | |
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| 1.2213 | 2.98 | 97 | 1.1364 | 0.5387 | 0.0450 | 0.9068 | 0.4759 | |
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| 1.149 | 4.0 | 130 | 1.1176 | 0.5543 | 0.0731 | 0.9132 | 0.4931 | |
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| 1.1558 | 4.98 | 162 | 1.1035 | 0.5630 | 0.0705 | 0.9303 | 0.5004 | |
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| 1.1363 | 6.0 | 195 | 1.1006 | 0.5655 | 0.1020 | 0.9113 | 0.5066 | |
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| 1.1138 | 6.98 | 227 | 1.0938 | 0.5699 | 0.1121 | 0.9113 | 0.5117 | |
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| 1.0807 | 8.0 | 260 | 1.0897 | 0.5742 | 0.1147 | 0.9170 | 0.5158 | |
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| 1.1071 | 8.98 | 292 | 1.0867 | 0.5757 | 0.1138 | 0.9202 | 0.5170 | |
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| 1.1017 | 9.85 | 320 | 1.0867 | 0.5757 | 0.1164 | 0.9183 | 0.5173 | |
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### Framework versions |
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- Transformers 4.29.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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