This project utilizes Natural Language Processing (NLP) techniques to predict and complete sentences based on a given input. Built with Python, TensorFlow, Keras, and OpenCV, this system can assist in writing by suggesting completions, enhancing user experience, and improving efficiency.

Sep 6, 2024 · In this article, we will learn about sentence autocompletion using TensorFlow. We will follow all the steps that are needed for MLOPs. We will start with importing and cleaning the text, to creating and fitting the model and then we will create a website using Flask framework.

Jun 20, 2023 · In this article, we are going to see how we can use NLP to autocomplete half-written sentences using deep learning methods. We will also see how we can generate clean data for training our NLP model. We will cover the following steps in this article. Cleaning the text data for training the NLP model; Loading the dataset using PyTorch

A Python-based NLP project utilizing the SwiftKey dataset (4 million tweets, blogs, and news) for text generation and language modeling. The project includes Data Pre-processing, Model Training, and Evaluation.

This project implements a Sentence Auto-Completion model using Natural Language Processing techniques. The model predicts the next word in a sequence based on the input context. It is built using TensorFlow and Keras and trained on a large text corpus. Text cleaning to …

Aug 8, 2023 · An autocomplete system is designed to predict and suggest the most likely next word or phrase based on the input provided by the user. This is achieved by analyzing large text corpora and...

Apr 14, 2024 · In this blog post, we will explore the implementation of a Sentence Auto-Completion system using LSTM (Long Short-Term Memory) deep learning architecture.

Jun 8, 2023 · By learning to predict the missing or corrupted tokens, the denoising autoencoder learns to extract meaningful features from the input sentence.

Dec 7, 2016 · One most recent work on this problem is Exploiting Linguistic Features for Sentence Completion. You can find many other closely related NLP applications that now-a-days being address by using deep learning techniques. You can consider them as advanced techniques based on language modeling.

An autocomplete can be helpful, faster, convenient and also correct any grammatical / spelling error at the same time. Project : In the jupyter notebook in this project, we select an history of sentenses written by the representatives and the customer, format and correct them using a few regex rules and count them so we can estimate their ...