Instructions to use Hemg/small-deepfake with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hemg/small-deepfake with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Hemg/small-deepfake")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Hemg/small-deepfake") model = AutoModelForAudioClassification.from_pretrained("Hemg/small-deepfake", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model save
Browse files
README.md
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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| No log | 0.8 | 2 |
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### Framework versions
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0003
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| No log | 0.8 | 2 | 0.8441 | 0.3 |
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### Framework versions
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