datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
piqaTo apply eyeshadow without a brush, should I use a cotton swab or a toothpick?
Questions requiring this kind of physical commonsense pose a challenge to state-of-the-art
natural language understanding systems. The PIQA dataset introduces the task of physical commonsense reasoning
and a corresponding benchmark dataset Physical Interaction: Question Answering or PIQA.
Physical commonsense knowledge is a major challenge on the road to true AI-completeness,
including robots that interact with the world and understand natural language.
PIQA focuses on everyday situations with a preference for atypical solutions.
The dataset is inspired by instructables.com, which provides users with instructions on how to build, craft,
bake, or manipulate objects using everyday materials.
The underlying task is formualted as multiple choice question answering:
given a question `q` and two possible solutions `s1`, `s2`, a model or
a human must choose the most appropriate solution, of which exactly one is correct.
The dataset is further cleaned of basic artifacts using the AFLite algorithm which is an improvement of
adversarial filtering. The dataset contains 16,000 examples for training, 2,000 for development and 3,000 for testing.piqa
Dataset Card for "Physical Interaction: Question Answering"
Dataset Summary
To apply eyeshadow without a brush, should I use a cotton swab or a toothpick?
Questions requiring this kind of physical commonsense pose a challenge to state-of-the-art
natural language understanding systems. The PIQA dataset introduces the task of physical commonsense reasoning
and a corresponding benchmark dataset Physical Interaction: Question Answering or PIQA.
Physical commonsense knowledge… See the full description on the dataset page: https://huggingface.co/datasets/lighteval/piqa.global-piqa-nonparallel
Global PIQA Non-Parallel
Global PIQA is a participatory commonsense reasoning benchmark for over 100 languages, constructed by hand by over 350 researchers from over 65 countries around the world.
The non-parallel split covers 136 language varieties, covering five continents, 18 language families, and 24 writing systems.
In this non-parallel split, over 50% of examples reference local foods, customs, traditions, or other culturally-specific elements.
Details are in our preprint:… See the full description on the dataset page: https://huggingface.co/datasets/mrlbenchmarks/global-piqa-nonparallel.global-piqa-parallel
Global PIQA Parallel
Global PIQA is a participatory commonsense reasoning benchmark for over 100 languages, constructed by hand by over 350 researchers from over 65 countries around the world.
The parallel split is a multi-parallel dataset for 131 language varieties, covering five continents, 16 language families, and 23 writing systems.
In this parallel split, each example was machine-translated from English, then manually corrected by a native speaker of the target language.… See the full description on the dataset page: https://huggingface.co/datasets/mrlbenchmarks/global-piqa-parallel.piqaThe PIQA dataset without needing to run remote code, so it is compatible with datasets >= 4.0.0.
piqaTo apply eyeshadow without a brush, should I use a cotton swab or a toothpick?
Questions requiring this kind of physical commonsense pose a challenge to state-of-the-art
natural language understanding systems. The PIQA dataset introduces the task of physical commonsense reasoning
and a corresponding benchmark dataset Physical Interaction: Question Answering or PIQA.
Physical commonsense knowledge is a major challenge on the road to true AI-completeness,
including robots that interact with the world and understand natural language.
PIQA focuses on everyday situations with a preference for atypical solutions.
The dataset is inspired by instructables.com, which provides users with instructions on how to build, craft,
bake, or manipulate objects using everyday materials.
The underlying task is formualted as multiple choice question answering:
given a question `q` and two possible solutions `s1`, `s2`, a model or
a human must choose the most appropriate solution, of which exactly one is correct.
The dataset is further cleaned of basic artifacts using the AFLite algorithm which is an improvement of
adversarial filtering. The dataset contains 16,000 examples for training, 2,000 for development and 3,000 for testing.piqa_ca
Dataset Card for piqa_ca
piqa_ca is a multiple choice question answering dataset in Catalan that has been professionally translated from the PIQA validation set in English.
Dataset Details
Dataset Description
piqa_ca (Physical Interaction Question Answering - Catalan) is designed to evaluate physical commonsense reasoning using question-answer triplets based on everyday situations. It includes 1838 instances in the validation split. Each instance contains… See the full description on the dataset page: https://huggingface.co/datasets/projecte-aina/piqa_ca.PIQA-eu
Dataset Card for PIQA-eu
Point of Contact: hitz@ehu.eus
Dataset Description
Dataset Summary
PIQA-eu is the professional translation to Basque of the PIQA's
(Bisk et al., 2020) validation partition.
PIQA is a commonsense QA benchmark for naive physics reasoning focusing on how we interact with everyday
objects in everyday situations.
Languages
eu-ES
Dataset Structure
Data Instances
PIQA-eu examples look like this:
{… See the full description on the dataset page: https://huggingface.co/datasets/HiTZ/PIQA-eu.piqaTo apply eyeshadow without a brush, should I use a cotton swab or a toothpick?
Questions requiring this kind of physical commonsense pose a challenge to state-of-the-art
natural language understanding systems. The PIQA dataset introduces the task of physical commonsense reasoning
and a corresponding benchmark dataset Physical Interaction: Question Answering or PIQA.
Physical commonsense knowledge is a major challenge on the road to true AI-completeness,
including robots that interact with the world and understand natural language.
PIQA focuses on everyday situations with a preference for atypical solutions.
The dataset is inspired by instructables.com, which provides users with instructions on how to build, craft,
bake, or manipulate objects using everyday materials.
The underlying task is formualted as multiple choice question answering:
given a question `q` and two possible solutions `s1`, `s2`, a model or
a human must choose the most appropriate solution, of which exactly one is correct.
The dataset is further cleaned of basic artifacts using the AFLite algorithm which is an improvement of
adversarial filtering. The dataset contains 16,000 examples for training, 2,000 for development and 3,000 for testing.piqa-bn
Dataset Summary
This is the translated version of the PIQA LLM evaluation dataset. The dataset was translated using a new method called Expressive Semantic Translation (EST), which combines Google Translation with LLM-based rewriting. PIQA introduces the task of physical commonsense reasoning and provides a corresponding benchmark for understanding physical interactions in everyday situations. It focuses on atypical solutions to practical problems, inspired by instructional guides… See the full description on the dataset page: https://huggingface.co/datasets/hishab/piqa-bn.piqa_yoruba_pidgin
Physical Commonsense Reasoning for Yorùbá and Nigerian Pidgin
Dataset Summary
This dataset was developed for the MRL 2025 Shared Task on Multilingual Physical Reasoning. For more details, see Global PIQA: Evaluating Physical Commonsense Reasoning Across 100+ Languages and Cultures.
It provides a test collection for evaluating physical commonsense reasoning, that is, a model's ability to understand how objects, actions, and outcomes relate in everyday scenarios.
The… See the full description on the dataset page: https://huggingface.co/datasets/taresco/piqa_yoruba_pidgin.piqa_ru
Dataset Card for "piqa_ru"
This is translated version of piqa dataset into Russian.
piqa-mt-pt
PIQA-PT
Portuguese machine translation of PIQA (Physical Interaction QA), a benchmark for physical commonsense reasoning.
Translated using a Finetuned GemmaX2-9B for pt-PT.
Original Dataset: https://huggingface.co/datasets/ybisk/piqa
Note: This dataset is machine translated and may contain translation errors or artifacts.
This dataset is provided as part of the AMALIA project and is included in AMALIA-Bench, a comprehensive benchmark suite for evaluating large… See the full description on the dataset page: https://huggingface.co/datasets/amalia-llm/piqa-mt-pt.piqa_es
Dataset Card for PIQA (Spanish Version)
Dataset summary
This dataset provides the Spanish translation and adaptation of the validation
set of PIQA (Physical Interaction: Question Answering). The original dataset
was designed to evaluate physical commonsense reasoning in language models
through questions about everyday situations. Each example presents a physical
goal and two possible solutions, only one of which is correct.
This Spanish adaptation enables… See the full description on the dataset page: https://huggingface.co/datasets/langtech-languagemodeling/piqa_es.piqa-ka
piqa-ka
Georgian translation of the PIQA (Physical Interaction QA) benchmark.
Dataset Summary
Property
Value
Examples
1,720
Splits
validation
Languages
Georgian, English
Task
Physical Commonsense Reasoning
Data Fields
goal: Goal description (English)
sol1: Solution 1 (English)
sol2: Solution 2 (English)
label: Correct solution index
goal_ka: Goal description (Georgian)
sol1_ka: Solution 1 (Georgian)
sol2_ka: Solution 2 (Georgian)… See the full description on the dataset page: https://huggingface.co/datasets/tbilisi-ai-lab/piqa-ka.piqaTo apply eyeshadow without a brush, should I use a cotton swab or a toothpick?
Questions requiring this kind of physical commonsense pose a challenge to state-of-the-art
natural language understanding systems. The PIQA dataset introduces the task of physical commonsense reasoning
and a corresponding benchmark dataset Physical Interaction: Question Answering or PIQA.
Physical commonsense knowledge is a major challenge on the road to true AI-completeness,
including robots that interact with the world and understand natural language.
PIQA focuses on everyday situations with a preference for atypical solutions.
The dataset is inspired by instructables.com, which provides users with instructions on how to build, craft,
bake, or manipulate objects using everyday materials.
The underlying task is formualted as multiple choice question answering:
given a question `q` and two possible solutions `s1`, `s2`, a model or
a human must choose the most appropriate solution, of which exactly one is correct.
The dataset is further cleaned of basic artifacts using the AFLite algorithm which is an improvement of
adversarial filtering. The dataset contains 16,000 examples for training, 2,000 for development and 3,000 for testing.piqa-ja-mbartm2m
Dataset Card for "piqa-ja-mbartm2m"
Dataset Description
This is the Japanese Translation version of piqa.
The translator used in it was facebook/mbart-large-50-many-to-many-mmt.
License
The same as the original piqa.
PiqaThis is dataset is just to support the latest datasets format. Please refer the original author's dataset for complete details.
https://huggingface.co/datasets/ybisk/piqa
piqa-mk
PIQA MK version
This dataset is a Macedonian adaptation of the PIQA dataset, originally curated (English -> Serbian) by Aleksa Gordić. It was translated from Serbian to Macedonian using the Google Translate API.
You can find this dataset as part of the macedonian-llm-eval GitHub and HuggingFace.
Why Translate from Serbian?
The Serbian dataset was selected as the source instead of English because Serbian and Macedonian are closer from a linguistic standpoint, making… See the full description on the dataset page: https://huggingface.co/datasets/LVSTCK/piqa-mk.