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The Next Word Predictor using LSTM is a project that builds a text prediction model using Long Short-Term Memory (LSTM) neural networks. It predicts the most likely next word in a given sequence, useful for text composition and natural language processing tasks. The project allows customizable training and includes an interactive script for testing
Text auto-completion system using the bert-base-uncased model by Hugging Face in the backend. Designed to enhance user experience across various applications, it anticipates and suggests word sequences as users type.
Text auto-completion system using the bert-base-uncased model by Hugging Face in the backend. Designed to enhance user experience across various applications, it anticipates and suggests word sequences as users type.
Next word prediction. aims to generate coherent and contextually relevant suggestions for the next word based on the patterns and relationships learned from training data.
Next word prediction using TensorFlow and NLP improves writing by suggesting the next word in messages, emails, and essays. It uses deep learning to analyze text data, predicting the most likely word based on context. This enhances typing speed and accuracy, aiding in coherent and efficient communication.
Predict the future words efficiently with the "Next Word Prediction Using Markov Model" project. Built in Python and powered by the `msvcrt` module, this academic initiative explores the Markov chain model to anticipate the most likely next word based on a given sequence.
A personalized autocomplete (next word prediction) project using three different architectures: stacked LSTMs, Seq2Seq with Attention and LSTMs and GPT-2, written from scratch.