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README.md
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- pytorch
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- text-classification
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- spam-detection
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task_categories:
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- text-classification
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datasets:
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## Important Notes
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- The model returns **logits** as output; to obtain probabilities, apply `torch.sigmoid`.
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- The model uses the `bert-base-uncased` tokenizer **only for tokenization** (the encoder is NOT BERT).
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## Files
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- `BiLSTMClassifier.safetensors`: trained weights
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- `BiLSTMClassifier.py`: model definition
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- pytorch
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- text-classification
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- spam-detection
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model_details:
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parameters: 4403585
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task_categories:
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- text-classification
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datasets:
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## Important Notes
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- The model returns **logits** as output; to obtain probabilities, apply `torch.sigmoid`.
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- The model uses the `bert-base-uncased` tokenizer **only for tokenization** (the encoder is NOT BERT).
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- Number of parameters: ~4.4M
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## Files
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- `BiLSTMClassifier.safetensors`: trained weights
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- `BiLSTMClassifier.py`: model definition
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