Repository navigation
extractive-question-answering
- Website
- Wikipedia
A robust, all-in-one GPT interface for Discord. ChatGPT-style conversations, image generation, AI-moderation, custom indexes/knowledgebase, youtube summarizer, and more!
AI Chatbot for analyzing/extracting information from data in conversational format.
Machine Reading Comprehension special for the Vietnamese language
Explore an emerging NLP capability with WikiQA, an automated question answering system built on top of Wikipedia.
Scripts for finetuning models for Question Answering task in Ukrainian
Extractive Question-Answering with BERT on SQuAD v2.0 (Stanford Question Answering Dataset) using NVIDIA PyTorch Lightning
A robust, all-in-one GPT3 interface for Discord. Chat just like ChatGPT right inside Discord! Generate beautiful AI art using DALL-E 2! Automatically moderate your server using AI!
Your Data as Knowledge Base + AI. ChatBot
Extractive Question Answering System
Extractive Nepali Question Answering System | Browser Extension & Web Application
LayoutLMv3 applied to a VQA problem with infographics.
KorSQuAD-pl provides transfer learning codes about korean dataset KorQuAD and english dataset SQuAD for extractive question answering. KorSQuAD-pl implemented through pytorch lightning.
The official repository for the PSYCHIC model
Official source codes for implementing "Accelerating materials language processing with large language models"
Qur'an QA 2023 Shared Task! Answering Questions on the Holy Qur'an @ ArabicNLP 2023, co-located with EMNLP 2023
DocBuddy is a Flask web app that lets users upload and interact with PDF files by summarizing content, suggesting keywords, and providing a basic Q&A feature, all through an intuitive interface.
This project implements an Extractive Question Answering (EQA) system that extracts answers from a set of downloaded text files based on user queries.
neural information retrieval and ranking & extractive question answering
Generate a response for the question from pre-defined text using LLM(Extracted Question-Answering(QA) Model).
This project implements an Extractive Question Answering (QA) system using Hugging Face Transformers.