# google-research-datasets/natural-questions

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1,124 stars · 163 forks · Python · Apache-2.0 · archived

## Links

- GitHub: https://github.com/google-research-datasets/natural-questions
- awesome-repositories: https://awesome-repositories.com/repository/google-research-datasets-natural-questions.md

## Description

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 document representations, it enables models to learn how to locate precise information within complex source materials rather than relying on simple text matching.

The dataset includes comprehensive tooling for benchmarking and validation, allowing researchers to measure model performance against verified reference data. It supports the entire lifecycle of question answering development, from training models on annotated queries to executing automated evaluation pipelines that ensure consistent and reproducible results.

## Tags

### Part of an Awesome List

- [Question Answering Datasets](https://awesome-repositories.com/f/awesome-lists/data/question-answering-datasets.md) — Serves as a comprehensive dataset of search queries and Wikipedia document spans for training and benchmarking question answering systems.
- [Question Answering Benchmarks](https://awesome-repositories.com/f/awesome-lists/learning/evaluation-benchmarks/question-answering-benchmarks.md) — Serves as a standardized benchmark dataset for evaluating open-domain question answering models. ([source](https://github.com/google-research-datasets/natural-questions/blob/master/README.md))
- [Question Answering Models](https://awesome-repositories.com/f/awesome-lists/ai/question-answering-models.md) — Provides the foundational data and tooling required to train and refine question answering systems. ([source](https://github.com/google-research-datasets/natural-questions#readme))
- [Answer Span Prediction](https://awesome-repositories.com/f/awesome-lists/ai/question-answering/answer-span-prediction.md) — Predicts the start and end positions of answers within source documents. ([source](https://github.com/google-research-datasets/natural-questions#readme))

### Artificial Intelligence & ML

- [Machine Learning Datasets](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/machine-learning-datasets.md) — Provides a large-scale corpus of real search queries and annotated document spans for training and evaluating machine learning models.
- [Document Information Extraction](https://awesome-repositories.com/f/artificial-intelligence-ml/document-information-extraction.md) — Identifies and marks precise text segments within documents to locate information.
- [Natural Language Processing Datasets](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/machine-learning-datasets/natural-language-processing-datasets.md) — Contains a large-scale collection of annotated text spans and queries designed to improve automated comprehension and information retrieval.
- [Model Performance Metrics](https://awesome-repositories.com/f/artificial-intelligence-ml/model-performance-metrics.md) — Calculates performance metrics to validate model output quality against reference fields. ([source](https://github.com/google-research-datasets/natural-questions/tree/master/nq_open))
- [Model Predictions](https://awesome-repositories.com/f/artificial-intelligence-ml/model-predictions.md) — Generates structured model predictions from input data for performance benchmarking. ([source](https://github.com/google-research-datasets/natural-questions/blob/master/competition.md))
- [Natural Language Processing](https://awesome-repositories.com/f/artificial-intelligence-ml/natural-language-processing.md) — Advances information retrieval technologies through the analysis of real search queries and document responses.

### Data & Databases

- [Precomputed Ground Truth Datasets](https://awesome-repositories.com/f/data-databases/static-benchmark-datasets/precomputed-ground-truth-datasets.md) — Provides high-quality, human-annotated ground truth datasets for training and benchmarking models.
- [Span Extraction Utilities](https://awesome-repositories.com/f/data-databases/text-processing-utilities/text-extraction/span-extraction-utilities.md) — Identifies and extracts specific text spans from documents to serve as precise answers.

### Software Engineering & Architecture

- [Evaluation Pipelines](https://awesome-repositories.com/f/software-engineering-architecture/training-pipelines/two-stage/evaluation-pipelines.md) — Provides automated evaluation pipelines that score model inference outputs against verified reference data.

### DevOps & Infrastructure

- [Model-to-Image Packaging](https://awesome-repositories.com/f/devops-infrastructure/container-orchestration/image-management-tools/container-image-distribution/model-to-image-packaging.md) — Bundles models and dependencies into container images for consistent evaluation. ([source](https://github.com/google-research-datasets/natural-questions/blob/master/competition.md))