hsinyuan-huang/FlowQA
Implementation of conversational QA model: FlowQA (with slight improvement)
This project helps AI/ML researchers and developers build conversational AI models that can answer follow-up questions by understanding the context of previous turns in a dialogue. It takes question-answer datasets as input and produces a trained model capable of sustained, context-aware Q&A. This is for those working on advancing conversational AI technology.
196 stars. No commits in the last 6 months.
Use this if you are an AI researcher or developer looking to experiment with and build upon a baseline model for conversational question answering that considers dialogue history.
Not ideal if you are an end-user seeking a ready-to-use chatbot or conversational AI application, as this requires significant technical expertise to set up and train.
Stars
196
Forks
54
Language
Python
License
—
Category
Last pushed
May 21, 2019
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/hsinyuan-huang/FlowQA"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
asahi417/lm-question-generation
Multilingual/multidomain question generation datasets, models, and python library for question...
SparkJiao/SLQA
An Unofficial Pytorch Implementation of Multi-Granularity Hierarchical Attention Fusion Networks...
MurtyShikhar/Question-Answering
TensorFlow implementation of Match-LSTM and Answer pointer for the popular SQuAD dataset.
allenai/aokvqa
Official repository for the A-OKVQA dataset
liamdugan/summary-qg
Code for the ACL 2022 Paper "A Feasibility Study of Answer-Agnostic Question Generation for Education"