Implement Text Generation using LSTM (NLP Model) #NLP

Implement a Text Generation model using LSTM (Long Short-Term Memory) networks as part of our NLP project. The goal is to train a recurrent neural network on a text dataset and generate coherent, context-aware text sequences.

Objectives:

• Preprocess and clean the text dataset (tokenization, lowercasing, removing punctuation, etc.)

• Prepare input sequences for training the LSTM model

• Build and train an LSTM-based neural network using frameworks like TensorFlow or PyTorch

• Generate text given an initial input seed

• Evaluate model performance (e.g., by examining generated text quality or using perplexity)

Tasks / To-Do:

• Collect and clean dataset for training

• Tokenize and vectorize text data

• Define and build LSTM model architecture

• Train the model and fine-tune hyperparameters

• Implement text generation logic (predict next characters/words)

• Save and load trained model for reuse

• Add documentation and usage instructions

Expected Outcome:

A trained LSTM model capable of generating human-like text sequences from a given prompt, with reproducible training code and results.

Tech Stack:

Python, Google Collab ( Code Editor), TensorFlow / Keras (or PyTorch)

NumPy, Pandas, Matplotlib (for data handling and visualization)

Additional Notes:

This issue serves as the foundation for the project’s NLP text generation module. Once implemented, enhancements like attention mechanisms or transformer-based models can be considered in future issues.