facebookresearch/DrQA

Health v2 · maintenance only

Flags: no_releases archived no_license

Adoption not part of the score

4468 stars · 881 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

DrQA is a PyTorch implementation of a system for open-domain question answering that combines document retrieval over Wikipedia with a neural machine comprehension model. It includes code, data, and pre-trained models, and can be applied to any large collection of unstructured documents.

Use cases

• answer factoid questions against wikipedia
• build an open-domain question answering system
• retrieve relevant documents from a large corpus then extract answer spans
• apply machine reading comprehension to my own document collection
• run an interactive qa demo over wikipedia
• evaluate a reading comprehension model on qa datasets

When to choose

• you need a classic retrieve-then-read open-domain QA pipeline with pre-trained models
• you want to run machine reading at scale over Wikipedia or a custom document set
• you need a research baseline for extractive question answering in PyTorch

When to avoid

• you want a modern LLM or RAG stack with vector embeddings rather than a 2017-era span-extraction model
• you need actively maintained software with recent updates and broad community support
• you need generative answers rather than extracted answer spans

Facets

library · maturity maintenance

nlp machine-learning search-engine rag artificial-intelligence large-language-models python question-answering reading-comprehension wikipedia pytorch document-retrieval open-domain-qa research natural-language-processing search linux macos

Member repositories

For agents

markdown · JSON · MCP: product_card(name="facebookresearch/DrQA")