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Seq2Seq Translation Model (English to Norwegian) Overview This project implements a Sequence-to-Sequence (Seq2Seq) model to translate sentences from English to Norwegian. Using PyTorch and the OPUS100 dataset, the model leverages LSTM-based encoder-decoder architecture to generate translations.
Highlights Dataset: English-Norwegian language pairs from OPUS100. Model: Embedding layer for word representation. LSTM-based encoder-decoder architecture. Linear layer for output predictions. Metrics: BLEU and ChrF scores to evaluate translation quality. Visualizations: Loss curves and evaluation scores plotted over training epochs. Workflow Data Preprocessing: Tokenization and padding of sequences for uniform input. Model Training: CrossEntropyLoss and Adam optimizer for efficient learning. Evaluation: BLEU and ChrF scores to measure performance. Model Saving: Trained weights stored as seq2seq_model.pth. Visualization: Loss trends and evaluation metrics visualized for insights.