Text Generation using LSTM

Introduction

• This project aims to generate relevant text from user-given seed text using LSTM (Long Short-Term Memory) algorithm.
• This model is trained upon a given corpus of text data to learn patterns, grammar, and contextual relationships.
• The model showcases its ability to generate coherent and contextually relevant text.

Methodology

The following steps ensure coherent and contextually relevant text generation:

1. Loading the data.
2. Preprocessing the data and Tokenizing.
3. Building and fitting the model on data.
4. Evaluating the model.
5. Predicting i.e.Generating the text and Iterate on the process.

Tools and Technologies involved:

• Numpy
• Pandas
• Tensorflow
• Matplotlib
• LSTM (Long Short-Term Memory) Algorithm

Flowcharts

• Methodology Involved:

• Plotting the Model:

NOTE:

The text generated by the model will be influenced by the Text Corpus on which the model is trained upon.