WinoGrande Raw Dataset
The WinoGrande Raw dataset offers a large collection of empty sentences with two options, intended to assess the ability of models to perform reasoning based on common sense. Inspired by the Winograd Schema Challenge, it offers increased robustness against biases specific to the initial dataset.
Description
WinoGrande Raw contains around 44,000 problems formulated into binary choice tasks where you have to select the right option to complete a sentence. Each example includes one sentence, two completion options, and the correct answer.
What is this dataset for?
- Evaluate and train models using common-sense reasoning
- Testing the robustness of models in the face of the classic biases of Winograd datasets
- Develop efficient NLP systems for contextual understanding
Can it be enriched or improved?
Yes, it is possible to enrich this dataset with additional annotations, examples in different languages, or reformulations to diversify use cases.
🔎 In summary
🧠 Recommended for
- NLP researchers
- Reasoning model developers
- AI R&D teams
🔧 Compatible tools
- Hugging Face Datasets
- PyTorch
- TensorFlow
- Scikit-learn
💡 Tip
Combine with other contextual understanding datasets for effective multi-task training.
Frequently Asked Questions
What is the main language of the dataset?
The dataset is mostly in English, with sentences designed to assess reasoning in English.
Can this dataset be used for fine-tuning?
Yes, it is perfectly suited for fine-tuning models on binary choice reasoning tasks.
Is the dataset subject to bias?
WinoGrande was designed to reduce the biases typical of the Winograd Schema Challenge, but vigilance is still recommended.
