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This project aims to predict the next words in a sentence using a language model trained on the Medium dataset, specifically focusing on generating likely sentences based on the initial words of a Medium post title entered in the search bar.
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.
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.
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
A program which guesses next words based on the user's input. Suggestions are the words with the highest probability to follow what has been already written, calculated in the n_grams of different size.
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.