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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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: ast-finetuned-audioset-10-10-0.4593_ft_env_aug_0-2 |
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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-finetuned-audioset-10-10-0.4593_ft_env_aug_0-2 |
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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: 0.6899 |
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- Accuracy: 0.9643 |
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- Precision: 0.9694 |
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- Recall: 0.9643 |
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- F1: 0.9631 |
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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-06 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 8 |
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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 | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 2.0165 | 1.0 | 28 | 1.6252 | 0.4643 | 0.5373 | 0.4643 | 0.4711 | |
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| 1.3702 | 2.0 | 56 | 1.0553 | 0.8571 | 0.8929 | 0.8571 | 0.8536 | |
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| 0.8861 | 3.0 | 84 | 0.6899 | 0.9643 | 0.9694 | 0.9643 | 0.9631 | |
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| 0.5655 | 4.0 | 112 | 0.4766 | 0.9643 | 0.9694 | 0.9643 | 0.9631 | |
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| 0.4232 | 5.0 | 140 | 0.3403 | 0.9643 | 0.9694 | 0.9643 | 0.9631 | |
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| 0.3148 | 6.0 | 168 | 0.2679 | 0.9643 | 0.9694 | 0.9643 | 0.9631 | |
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| 0.2335 | 7.0 | 196 | 0.2239 | 0.9643 | 0.9694 | 0.9643 | 0.9631 | |
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| 0.176 | 8.0 | 224 | 0.1979 | 0.9643 | 0.9694 | 0.9643 | 0.9631 | |
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| 0.1624 | 9.0 | 252 | 0.1824 | 0.9643 | 0.9694 | 0.9643 | 0.9631 | |
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| 0.1466 | 10.0 | 280 | 0.1781 | 0.9643 | 0.9694 | 0.9643 | 0.9631 | |
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### Framework versions |
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- Transformers 4.27.4 |
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- Pytorch 2.0.0 |
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- Datasets 2.10.1 |
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- Tokenizers 0.11.0 |
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