LSTM-AIChatPersian: A Deep Learning-Based Persian Chatbot

LSTM-AIChatPersian: A Deep Learning-Based Persian Chatbot

This project is designed to develop a sophisticated conversational AI model tailored for the Persian language. Using Long Short-Term Memory (LSTM) networks, the model is capable of engaging in meaningful dialogues, answering questions, and providing intelligent, context-aware responses. It employs advanced Natural Language Processing (NLP) techniques, including text tokenization, sequence modeling, and attention mechanisms, to achieve high accuracy and fluency in understanding and generating Persian text.

Key Features and Objectives:

. Persian Language Processing: This model is specifically optimized for the Persian language, taking into account its unique grammar, syntax, and vocabulary.

. LSTM Network: Utilizing LSTM layers to ensure efficient handling of long-term dependencies in conversational contexts.

. Contextual Understanding: The AI can track and maintain conversation context, offering more relevant and coherent responses.

. Customizable: Easily adaptable to different domains or datasets, allowing for use in a wide range of applications, from customer support to virtual assistants.

. State-of-the-Art Techniques: Incorporates cutting-edge NLP and deep learning strategies, including data augmentation, hyperparameter tuning, and model optimization techniques.

. Robust Evaluation: The model is evaluated using well-known NLP metrics such as BLEU and ROUGE to ensure high-quality output.

-The goal of this project is to push the boundaries of Persian-language AI, delivering a powerful tool for conversational interfaces while ensuring privacy and usability across different use cases.