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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_21-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_21-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: 2.5318 |
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- Accuracy: 0.6797 |
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- Sensitivity: 0.5322 |
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- Specificity: 0.8118 |
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- Score: 0.6720 |
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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: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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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: 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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| 0.8802 | 1.0 | 345 | 0.9189 | 0.6355 | 0.3236 | 0.9148 | 0.6192 | |
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| 0.8729 | 2.0 | 690 | 0.8915 | 0.6283 | 0.5138 | 0.7308 | 0.6223 | |
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| 0.6646 | 3.0 | 1035 | 0.9005 | 0.6551 | 0.6043 | 0.7005 | 0.6524 | |
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| 0.3145 | 4.0 | 1380 | 1.1884 | 0.6572 | 0.4018 | 0.8860 | 0.6439 | |
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| 0.2176 | 5.0 | 1725 | 1.4167 | 0.6623 | 0.5828 | 0.7335 | 0.6582 | |
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| 0.1556 | 6.0 | 2070 | 1.9695 | 0.6732 | 0.5061 | 0.8228 | 0.6645 | |
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| 0.0144 | 7.0 | 2415 | 2.3115 | 0.6761 | 0.5506 | 0.7885 | 0.6695 | |
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| 0.0001 | 8.0 | 2760 | 2.4443 | 0.6746 | 0.5291 | 0.8049 | 0.6670 | |
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| 0.0001 | 9.0 | 3105 | 2.5163 | 0.6775 | 0.5291 | 0.8104 | 0.6698 | |
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| 0.0001 | 10.0 | 3450 | 2.5318 | 0.6797 | 0.5322 | 0.8118 | 0.6720 | |
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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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