PREDICTIVE TEXT GENERATION WITH LONG SHORT-TERM MEMORY BASED LANGUAGE MODELS

This project focuses on building a predictive model for next-word generation using deep learning Spreprocessing a text dataset to remove noise, punctuation, and irrelevant symbols, ensuring the text is ready for tokenization. A tokenizer is employed to convert the text into sequences of tokens, mapping each word to a unique index, and generating input sequences for model training. The data is then prepared by padding sequences to ensure uniform length and splitting them into input-output pairs for training. The core of the model is an LSTM-based neural network, designed to handle sequential data effectively by capturing temporal dependencies in text. The model predicts the next word in a sequence by training on these tokenized sequences. The architecture includes embedding layers for word representations, LSTM layers to process sequences, and dense layers for classification of the next word. The trained model is then evaluated for accuracy and saved for future use. To make the model accessible and interactive, a user interface is created, allowing users to input a sentence and receive a prediction for the next word. This interface leverages the trained model and tokenizer to generate predictions in real time, providing a practical tool for exploring language generation and aiding in various applications like writing assistance and text prediction.