Natural Questions is a large-scale machine learning research dataset designed for training and evaluating open-domain question answering systems. It consists of a corpus of real search queries paired with human-annotated Wikipedia document spans, providing a standardized foundation for advancing automated information retrieval and comprehension technologies. The project distinguishes itself by providing high-quality ground truth data that supports multiple answer formats, including binary, short-form, and long-form responses. By incorporating extractive span annotations and structured documen
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
This the original implementation of Bi-directional Attention Flow for Machine Comprehensionpaper (Seo et al., 2016). - This is tensorflow v1.1.0 comaptible version. This is not compatible with previous trained models, so if you want to use them, go to v0.2.1v0.2.1. - The CodaLab…
A Tensorflow implementation of Google's QANet (previously Fast Reading Comprehension (FRC)) from ICLR2018. (Note: This is not an official implementation from the authors of the paper)
The main features of nlplearn/qanet are: Question Answering Models.
Open-source alternatives to nlplearn/qanet include: google-research-datasets/natural-questions — Natural Questions is a large-scale machine learning research dataset designed for training and evaluating open-domain… chinawithfrank/chatbotcourse — This project is a development course and learning curriculum focused on building large language model chatbots. It… hitvoice/drqa — DrQA A pytorch implementation of the ACL 2017 paper Reading Wikipedia to Answer Open-Domain Questions (DrQA). hkust-knowcomp/r-net — A Tensorflow implementation of R-NET: MACHINE READING COMPREHENSION WITH SELF-MATCHING NETWORKS. This project is… wissam-sib/dilbert — This repository contains the code to reproduce the results from the paper Delaying Interaction Layers in… xwhan/proqa — Resource-efficient method for pretraining a dense corpus index for open-domain QA and IR. Given a question, you could…