Omkar2118/datasets huggingface dataset:
QuaRTz
Full name: QuaRTz Dataset

QuaRTz is a crowdsourced dataset of 3864 multiple-choice questions about open domain qualitative relationships. Each question is paired with one of 405 different background sentences (sometimes short paragraphs).

The QuaRTz dataset V1 contains 3864 questions about open domain qualitative relationships. Each question is paired with one of 405 different background sentences (sometimes short paragraphs).

The dataset is split into train (2696), dev (384) and test (784). A background sentence will only appear in a single split.

Each line in a dataset file is a question specified as a json object, e.g., (with extra whitespace for readability).

Source: [Allen Institute for AI](https://allenai.org/data/quartz)

Introducing paper: QuaRTz: An Open-Domain Dataset of Qualitative Relationship Questions
WIQA
Full name: What-If Question Answering

The WIQA dataset V1 has 39705 questions containing a perturbation and a possible effect in the context of a paragraph. The dataset is split into 29808 train questions, 6894 dev questions and 3003 test questions.

Source: [Allen Institute for AI](https://allenai.org/data/wiqa)

Introducing paper: WIQA: A dataset for "What if..." reasoning over procedural text
QuaRel
Full name: QuaRel

QuaRel is a crowdsourced dataset of 2771 multiple-choice story questions, including their logical forms.

Source: [Allen Institute for AI](https://allenai.org/data/quarel)

Introducing paper: QuaRel: A Dataset and Models for Answering Questions about Qualitative Relationships
ProPara

The **ProPara** dataset is designed to train and test comprehension of simple paragraphs describing processes (e.g., photosynthesis), designed for the task of predicting, tracking, and answering questions about how entities change during the process.

ProPara aims to promote the research in natural language understanding in the context of procedural text. This requires identifying the actions described in the paragraph and tracking state changes happening to the entities involved. The comprehension task is treated as that of predicting, tracking, and answering questions about how entities change during the procedure. The dataset contains 488 paragraphs and 3,300 sentences. Each paragraph is richly annotated with the existence and locations of all the main entities (the “participants”) at every time step (sentence) throughout the procedure (~81,000 annotations).

ProPara paragraphs are natural (authored by crowdsourcing) rather than synthetic (e.g., in bAbI). Workers were given a prompt (e.g., “What happens during photosynthesis?”) and then asked to author a series of sentences describing the sequence of events in the procedure. From these sentences, participant entities and their existence and locations were identified. The goal of the challenge is to predict the existence and location of each participant, based on sentences in the paragraph.

Source: [Allen Institute for AI](https://allenai.org/data/propara)

Introducing paper: Tracking State Changes in Procedural Text: A Challenge Dataset and Models for Process Paragraph Comprehension
ScienceExamCER

ScienceExamCER is a collection of resources for studying explanation-centered inference, including explanation graphs for 1,680 questions, with 4,950 tablestore rows, and other analyses of the knowledge required to answer elementary and middle-school science questions.

Source: [Allen Institute for AI](https://allenai.org/data/explanationbank)

Introducing paper: ScienceExamCER: A High-Density Fine-Grained Science-Domain Corpus for Common Entity Recognition
TupleInf Open IE Dataset
Full name: TupleInf Open IE Dataset

The TupleInf Open IE dataset contains Open IE tuples extracted from 263K sentences that were used by the solver in “Answering Complex Questions Using Open Information Extraction” (referred as Tuple KB, T). These sentences were collected from a large Web corpus using training questions from 4th and 8th grade as queries. This dataset contains 156K sentences collected for 4th grade questions and 107K sentences for 8th grade questions. Each sentence is followed by the Open IE v4 tuples using their simple format.

Source: [Allen Institute for AI](https://allenai.org/data/tuple-ie)

Introducing paper: Answering Complex Questions Using Open Information Extraction