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---
license: bsd-3-clause
base_model: MIT/ast-finetuned-audioset-10-10-0.4593
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: ast-finetuned-audioset-10-10-0.4593-finetuned-common_voice
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ast-finetuned-audioset-10-10-0.4593-finetuned-common_voice
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.
It achieves the following results on the evaluation set:
- Loss: 0.5704
- Accuracy: 0.8775
- F1: 0.8773
- Recall: 0.8775
- Precision: 0.8773
- Mcc: 0.8469
- Auc: 0.9835
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | Mcc | Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|:------:|:------:|
| 1.0165 | 1.0 | 200 | 1.2394 | 0.5575 | 0.4931 | 0.5575 | 0.4662 | 0.4677 | 0.8879 |
| 0.8119 | 2.0 | 400 | 0.5971 | 0.7575 | 0.7576 | 0.7575 | 0.7668 | 0.6992 | 0.9592 |
| 0.1652 | 3.0 | 600 | 0.5719 | 0.815 | 0.8161 | 0.8150 | 0.8273 | 0.7711 | 0.9713 |
| 0.0829 | 4.0 | 800 | 0.8850 | 0.8 | 0.8017 | 0.8 | 0.8346 | 0.7590 | 0.9730 |
| 0.0102 | 5.0 | 1000 | 0.7974 | 0.8375 | 0.8386 | 0.8375 | 0.8590 | 0.8024 | 0.9778 |
| 0.0004 | 6.0 | 1200 | 0.5919 | 0.86 | 0.8607 | 0.86 | 0.8632 | 0.8254 | 0.9815 |
| 0.0 | 7.0 | 1400 | 0.5652 | 0.88 | 0.8798 | 0.8800 | 0.8803 | 0.8502 | 0.9833 |
| 0.0 | 8.0 | 1600 | 0.5665 | 0.875 | 0.8749 | 0.875 | 0.8749 | 0.8438 | 0.9834 |
| 0.0 | 9.0 | 1800 | 0.5695 | 0.8775 | 0.8773 | 0.8775 | 0.8773 | 0.8469 | 0.9835 |
| 0.0 | 10.0 | 2000 | 0.5704 | 0.8775 | 0.8773 | 0.8775 | 0.8773 | 0.8469 | 0.9835 |
### Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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