This project is a development course and learning curriculum focused on building large language model chatbots. It provides a structured series of tutorials for creating conversational agents through the application of natural language processing and deep learning models. The materials include a technical walkthrough for implementing neural networks and word embeddings to handle automated question-answering tasks. It also provides a guide for constructing large-scale conversation corpora from external text sources to train and evaluate dialogue systems. The curriculum covers core text analys
This project is a transformer-based language model and natural language processing toolkit designed to generate deep contextual representations of text. By utilizing a transformer-based encoder architecture, the system processes input sequences through stacked self-attention layers to capture the semantic meaning of tokens based on their surrounding sentence structure. The model distinguishes itself through bidirectional contextual processing, which analyzes text in both directions simultaneously, and masked language modeling, which trains the system by predicting hidden tokens within a seque
DrQA A pytorch implementation of the ACL 2017 paper Reading Wikipedia to Answer Open-Domain Questions (DrQA).
A Tensorflow implementation of R-NET: MACHINE READING COMPREHENSION WITH SELF-MATCHING NETWORKS. This project is specially designed for the SQuAD dataset. Should you have any question, please contact Wenxuan Zhou (wzhouad@connect.ust.hk).