Jarvis AI is a Python Module which is able to perform task like Chatbot, Assistant etc. It provides base functionality for any assistant application. This JarvisAI is built using Tensorflow, Pytorch, Transformers and other opensource libraries and frameworks.

Building a Chatbot in PyTorch using Transformers

A PyTorch Implementation of japanese chatbot using BERT and Transformer's decoder

[NLP] A ChatBot implemented using huggingface-transformers-gpt2

Chatbot using Tensorflow (Model is transformer) ko

A collection of 19 generative AI projects in Python, showcasing applications in text generation, image synthesis, and chatbots using frameworks like Transformers and PyTorch. Includes datasets, code, and tutorials for building and deploying cutting-edge AI models.

A chatbot using the Vaswani transformer as it's sequence-to-sequence module

ChatGPT (Chat Generative Pre-trained Transformer) is a chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large language models and has been fine-tuned (an approach to transfer learning) using both supervised and reinforcement learning techniques.

Chatbot in spanish using differents model: Seq2Seq model with Luong attention and transformer

Chat with Your Data App using Langchain, ChromaDB, Sentence Transformers, and LaMiNi LM Model. This Chatbot is completely powered by Open Source Models. No OpenAI key is required.

Conversational AI chatbot with consistent persona. Implementation of Seq2Seq, Transformer and Multiple Encoders in Python. Backend using Flask.

Training a chatbot using a transformer.

This is the source code of article how to create a chatbot in python . i.e A chatbot using the Reformer, also known as the efficient Transformer, to generate dialogues between two bots.

NLP projects including sentiment analysis, text summarization, and chatbot development using deep learning and transformers.

The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more. AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career. Along the way, you will also get career advice from deep learning experts from industry and academia.

This repository contains a layout how to build a general purpose chatbot using GPT3

Implementation of Basic Conversational Agent(a.k.a Chatbot) using PyTorch Transformer Module

A simple chatbot built using the vanilla Transformer architecture (Vaswani et al., 2017). It uses the encoder-decoder attention mechanism to learn basic conversational patterns from paired text data.

🤖 Transformer Chatbot web app using Tensorflow 2.0 and Django

An NLP-powered chatbot that processes user messages using rule-based responses and AI-generated replies via Hugging Face's Transformers.

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Implementation of state-of-the-art NLP models using transformers for tasks including machine translation, text-summarization, chatbots, and question answering.

Python Telegram chatbot API using generative pretraining transformers

ChatGPT (Chat Generative Pre-trained Transformer) is a chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large language models and has been fine-tuned (an approach to transfer learning) using both supervised and reinforcement learning techniques.

An LLM-based chatbot trained on philosophical texts built using OpenAI GPT models, Langchain Tokenizers, HuggingFace transformers, and Meta BART summarization model wrapped in a NextJS web app hosted completely on AWS

This is a medical Chatbot built using Llama2-7B and Sentence Transformers. The bot is powered by Langchain.

This repository provides code and tools for fine-tuning Phi-3.5-mini-instruct with QLoRA to produce contextually relevant chatbot responses using PyTorch and transformers. It includes dataset preparation, adaptive training with early stopping, Discord notifications, and a CLI for inference.

This project is a lightweight conversational AI chatbot that runs entirely on your local machine using Hugging Face Transformers and Chainlit. It loads a small language model (like facebook/opt-350m) for generating responses and provides a real-time chat interface in the browser.

A chatbot is a type of AI software, developed for the purpose of simulating a conversation with the user. They converse in natural language via messaging applications, websites, or any other form of communication platform. Some chatbots use extensive word-classification processes, Natural Language processors, and sophisticated AI, while others simply scan for general keywords and generate responses using common phrases obtained from an associated library or database. In our project, we have developed a model where the user can create a chatbot specific to a character. The user will be able to interact with the chatbot by giving it a few personality traits and the chatbot will be able to hold a conversation accordingly. If a personality is not specified then the chatbot has been given a default character - ‘Joey’ from the T.V show ‘Friends’. We do this by using Simple Transformers Model along with the pre-trained model provided by Hugging Face. This includes the training dataset for various personality traits; but since we decided to give the chatbot a unique default character, we trained the model and fine-tuned it to a dataset we created ourselves in the same JSON format from every episode’s script of the show. Lastly we used Tkinter package to build the GUI for the chatbot and connected it to our trained model and the chatbot was ready to chat.

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