winogrande
44k binary-choice fill-in-the-blank problems for commonsense reasoning.
Open dataset Updated 2026-06-18
What is winogrande?
WinoGrande is a collection of 44k problems formulated as binary-choice fill-in-the-blank tasks. Each example supplies a sentence containing a blank and two options; solving it depends on commonsense reasoning rather than superficial cues.
The dataset functions as an NLP benchmark for evaluating model performance on commonsense understanding and is suitable for training or testing systems that handle Winograd-style pronoun resolution and related inference problems.
What you can build with winogrande
Benchmarking commonsense reasoning
Evaluate language models on 44k binary fill-in-the-blank problems to measure performance on tasks requiring world knowledge while resisting annotation artifacts.
Fine-tuning for multiple-choice QA
Train or adapt models to select the correct option in sentence-completion scenarios that test everyday reasoning.
Dataset bias studies
Analyze and compare model behavior across WinoGrande splits versus earlier Winograd Schema datasets to quantify robustness improvements.
Load winogrande
Python
from datasets import load_dataset
ds = load_dataset("allenai/winogrande")- 1pip install datasets
- 2from datasets import load_dataset
- 3ds = load_dataset('allenai/winogrande')
- 4Access ds['train'] and ds['validation'] splits
- 5Format examples for model input as sentence + option pairs
winogrande: pros & cons
Pros
- +44k scale with reduced bias
- +Binary format simplifies evaluation
- +Directly targets commonsense gaps
- +Publicly hosted on Hugging Face
Cons
- –English only
- –Still solvable by some statistical shortcuts
- –Binary labels limit granularity
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Frequently asked questions
A 44k-example collection of fill-in-the-blank problems for commonsense reasoning, designed to improve on the original Winograd Schema Challenge.
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