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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: 0.0088
- Accuracy: 0.995
- F1: 0.9950
- Recall: 0.9950
- Precision: 0.9951
- Mcc: 0.9938
- Auc: 1.0000
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|:------:|:------:|
| 0.3329 | 1.0 | 200 | 0.1555 | 0.9525 | 0.9521 | 0.9525 | 0.9566 | 0.9418 | 0.9991 |
| 0.1007 | 2.0 | 400 | 0.1966 | 0.9525 | 0.9512 | 0.9525 | 0.9559 | 0.9420 | 0.9975 |
| 0.0243 | 3.0 | 600 | 0.0619 | 0.98 | 0.9799 | 0.9800 | 0.9805 | 0.9752 | 0.9999 |
| 0.0007 | 4.0 | 800 | 0.0194 | 0.995 | 0.9950 | 0.9950 | 0.9950 | 0.9938 | 1.0000 |
| 0.0 | 5.0 | 1000 | 0.0182 | 0.9925 | 0.9925 | 0.9925 | 0.9927 | 0.9907 | 1.0000 |
| 0.0 | 6.0 | 1200 | 0.0106 | 0.995 | 0.9950 | 0.9950 | 0.9951 | 0.9938 | 1.0000 |
| 0.0 | 7.0 | 1400 | 0.0098 | 0.995 | 0.9950 | 0.9950 | 0.9951 | 0.9938 | 1.0000 |
| 0.0 | 8.0 | 1600 | 0.0093 | 0.995 | 0.9950 | 0.9950 | 0.9951 | 0.9938 | 1.0000 |
| 0.0 | 9.0 | 1800 | 0.0089 | 0.995 | 0.9950 | 0.9950 | 0.9951 | 0.9938 | 1.0000 |
| 0.0 | 10.0 | 2000 | 0.0088 | 0.995 | 0.9950 | 0.9950 | 0.9951 | 0.9938 | 1.0000 |
### Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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