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README.md
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---
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library_name: transformers
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license: apache-2.0
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base_model: Heem2/Deepfake-audio-detection
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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: deepfake-audio-detector_V2
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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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# deepfake-audio-detector_V2
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This model is a fine-tuned version of [Heem2/Deepfake-audio-detection](https://huggingface.co/Heem2/Deepfake-audio-detection) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1666
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- Accuracy: 0.9897
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- Precision: 0.9912
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- Recall: 0.9882
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- F1: 0.9897
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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: 2
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- total_train_batch_size: 8
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 100
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- num_epochs: 5
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- mixed_precision_training: Native AMP
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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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| 0.1668 | 1.0 | 340 | 0.2081 | 0.9853 | 0.9853 | 0.9853 | 0.9853 |
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| 0.4339 | 2.0 | 680 | 0.2996 | 0.9809 | 0.9823 | 0.9794 | 0.9809 |
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| 0.0 | 3.0 | 1020 | 0.1666 | 0.9897 | 0.9912 | 0.9882 | 0.9897 |
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### Framework versions
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- Transformers 4.51.3
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- Pytorch 2.6.0+cu124
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- Datasets 3.6.0
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- Tokenizers 0.21.1
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