Text Generation AI - Next Word Prediction in Python
This repository contains a Python project that demonstrates how to predict the next word in a given text sequence using an LSTM (Long Short-Term Memory) model. The model is trained on a sequence of words to learn the context and predict the most likely next word in the sequence.
About the Project
Text generation and next word prediction are important tasks in natural language processing (NLP) with applications in chatbots, auto-completion, and content creation. This project leverages LSTM, a type of recurrent neural network (RNN) capable of learning long-term dependencies, to predict the next word in a text sequence. By training on large amounts of text data, the model learns the context and structure of language, enabling it to make accurate predictions.
Problem Statement
The challenge addressed in this project is predicting the next word in a given text sequence. Traditional text generation methods rely on predefined rules and templates, which can be inflexible and limited. This project aims to automate and improve the process using machine learning, providing more natural and contextually relevant word predictions.
Importance
Enhanced User Experience: Next word prediction is crucial in applications like predictive text input, providing users with faster and more accurate typing experiences.
Automation and Efficiency: Automating text generation tasks saves time and effort, especially in content creation and data entry.
Improved Communication: In chatbots and virtual assistants, accurate next word prediction enhances the naturalness and coherence of conversations, leading to better user interactions.
Educational Tools: This project can serve as an educational tool for learning about NLP and machine learning, demonstrating the application of LSTM models in real-world scenarios.
Getting Started
Python 3.x, numpy, nltk, tensorflow
Dataset Link : https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset
Data Preparation
The script tokenizes the text using a regular expression pattern and creates input-output sequences for training the model. It then converts these sequences into numerical format, suitable for training the LSTM model.
Model Training
The script defines an LSTM model using Keras, compiles it with the RMSprop optimizer, and trains it on the tokenized text sequences. The model learns the context and patterns in the text, enabling it to predict the next word in a sequence.
Prediction
The predict_next_word function takes an input text sequence and uses the trained LSTM model to predict the next word. It tokenizes the input text, converts it into the appropriate format, and feeds it into the model for prediction.
Results In this project i get 100 % accuracy