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Verified signatureBilingual Language Model for Next Token Prediction Overview: This project focuses on building a neural network-based language model for next token prediction using two languages: English and Hebrew. The model is implemented using an LSTM (Long Short-Term Memory) architecture, designed to predict the next word in a sequence based on the training data provided. The project leverages Recurrent Neural Networks (RNNs) and evaluates the model using the perplexity metric to measure the quality of the predictions. The final model and checkpoints are provided, along with training history including perplexity and loss values. Model Architecture: - Embedding Layer**: Converts tokenized words into dense vector representations. - LSTM Layer**: Consists of 128 units to capture long-term dependencies in the sequence data. - Dense Output Layer**: Outputs a probability distribution over the vocabulary to predict the next word. - Total Vocabulary Size**: The model is trained on a corpus of size `[tot…56bebd8