• Rutuja Gosavi
• Pradnya Konduskar
• Pranav Chavan
• Jyoti Kundale

With Next Word Prediction, also known as Language Modeling, is the task of predicting the next word. It is one of the most important tasks in NLP and has a wide range of applications. Attempting to create a model using the Nietzsche default text record that will predict the clients’ sentence after they have written n no of letters, the model will comprehend n letters and predict the top words using RNN neural network and Tensor Flow. Our goal in developing this model was to predict 10 or more words in the shortest amount of time possible. Because RNN has a long short-term memory, it can understand previous content and forecast words, which can assist the user to construct phrases. Also, the model is trained in such a way that it can understand Hinglish language and also can predict words based on it. We present a Bi- Directional Long short term memory network (BI-LSTM) which is a special kind of Neural Network (RNN) in which our thing is to prognosticate the coming word for a given set of words in the model.

RNN, LSTM, NLP, Text Prediction, Bi-directional LSTM

"NEXT WORD PREDICTION USING RECURRENT NEURAL NETWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 8, page no.192-197, August-2022, Available :http://www.jetir.org/papers/JETIRFQ06036.pdf

"NEXT WORD PREDICTION USING RECURRENT NEURAL NETWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 8, page no. pp192-197, August-2022, Available at : http://www.jetir.org/papers/JETIRFQ06036.pdf