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This model is a sequence-to-sequence (seq2seq) model for text summarization. It uses a bidirectional LSTM encoder and an LSTM decoder to generate summaries from input articles. The model was trained on a dataset with sequences of length up to 800 tokens.
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## Model Architecture
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### Encoder
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This model is a sequence-to-sequence (seq2seq) model for text summarization. It uses a bidirectional LSTM encoder and an LSTM decoder to generate summaries from input articles. The model was trained on a dataset with sequences of length up to 800 tokens.
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## Dataset
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CNN-DailyMail News Text Summarization from kaggle
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## Model Architecture
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### Encoder
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