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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
Paper-qa is a retrieval augmented generation system designed for question answering and analysis of scientific literature and technical documents. It functions as an LLM-powered research assistant that extracts grounded answers and summaries with citations from a document library. The system utilizes an agentic RAG orchestrator to iteratively refine search queries and gather evidence through multi-step tool calling. It features a multimodal document parser that extracts text, tables, and images from PDFs, alongside a vector-based indexer that embeds and caches document libraries for efficient
MindSearch is an LLM-based multi-agent search engine that decomposes complex user questions into targeted sub-queries and routes each to a specialized agent for parallel investigation. The system orchestrates multiple agents through a large language model, coordinating their tasks and interpreting search results to produce coherent answers from multiple sources. The project provides a configurable search backend interface that allows switching between Google, DuckDuckGo, Brave, and Bing search APIs by updating a configuration attribute. It includes a terminal-based debug interface for testing
nlp-recipes is a collection of implementation guides and reference templates for applying natural language processing techniques to real-world tasks. It provides standardized workflows and code examples for developing NLP pipelines, from dataset preparation and model training to performance evaluation. The project focuses on the practical application of transformer-based models, offering patterns for fine-tuning pretrained architectures for tasks such as text classification, named entity recognition, and question answering. It also includes a toolkit for model interpretability, allowing users
Tools for using Maluuba's news questions and answer data. The code in the repo is used to compile the dataset since it cannot be made directly available due to legal reasons.
The main features of maluuba/newsqa are: Question Answering, Question Answering Datasets.
Projects with overlapping indexed features include: google-research-datasets/natural-questions — Natural Questions is a large-scale machine learning research dataset designed for training and evaluating open-domain… future-house/paper-qa — Paper-qa is a retrieval augmented generation system designed for question answering and analysis of scientific… reorproject/reor — Reor is a local AI knowledge management application that stores, links, and searches personal notes using large… internlm/mindsearch — MindSearch is an LLM-based multi-agent search engine that decomposes complex user questions into targeted sub-queries… microsoft/nlp-recipes — nlp-recipes is a collection of implementation guides and reference templates for applying natural language processing… zyds/transformers-code — This project is a collection of scripts and workflows for training, fine-tuning, and deploying large language models…