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README.md
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---
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license: apache-2.0
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base_model: Bisher/wav2vec2_ASV_deepfake_audio_detection
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tags:
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- generated_from_trainer
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model-index:
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- name: short_name
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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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# short_name
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This model is a fine-tuned version of [Bisher/wav2vec2_ASV_deepfake_audio_detection](https://huggingface.co/Bisher/wav2vec2_ASV_deepfake_audio_detection) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- eval_loss: 0.5552
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- eval_accuracy: 0.895
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- eval_precision: 0.9061
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- eval_recall: 0.895
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- eval_f1: 0.8500
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- eval_TP: 1
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- eval_TN: 178
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- eval_FN: 21
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- eval_FP: 0
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- eval_EER: 0.2727
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- eval_min_tDCF: 0.0281
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- eval_auc_roc: 0.7296
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- eval_runtime: 66.7052
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- eval_samples_per_second: 2.998
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- eval_steps_per_second: 2.998
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- epoch: 0.04
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- step: 1
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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: 1
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- eval_batch_size: 1
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 4
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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: 2
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- mixed_precision_training: Native AMP
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### Framework versions
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- Transformers 4.44.0
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- Pytorch 2.4.0+cpu
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- Datasets 2.21.0
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- Tokenizers 0.19.1
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