Dataset: BoolQ (Yes/No Question Answering)

About this Dataset

BoolQ (Yes/No Question Answering)

15942 Yes / No Questions & Answers

By Huggingface Hub [source]

About this dataset

With this incredible dataset, researchers can now develop Natural Learning Processing applications that are based on logical reasoning and inference. This dataset includes two splitted files that each contain questions, answers and passages related to logical thinking. All the contents are taken from a variety of sources such as books, magazines, web pages and other sources.
The Boolq Dataset is perfect for anyone who wants to journey further into unlocking their ability for smart decision making by sharpening their analytical skills through its real-life problems. It presents an opportunity for individuals or groups of people alike who want to take a closer look through its columns - question, answer and passage – making it even easier to see how deep knowledge can come from precise inference with this collection! Step up your intelligence game today by discovering how logic can easily solve life’s puzzles with the Boolq Dataset!

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How to use the dataset

The Boolq Dataset is a collection of questions and answers related to logical thinking. This dataset is perfect for developing Natural Language Processing applications that rely on advanced reasoning abilities. Here we provide a guide to help you get started with the dataset quickly.

The Boolq dataset consists of two separate datasets, a training set and a validation set. The training set contains questions and answers related to logical thinking as well as passages associated with them. It can be used to train models using Natural Language Processing techniques such as supervised learning, recurrent neural networks, etc., in order to build applications that are able use complex logical inferences for solving problems. On the other hand, the validation set consists of only question-answer pairs without any associated passages which can be used for evaluating models once they have been trained on the training sets.

**Using the Dataset **
The Boolq dataset is composed of three columns: question, answer and passage (in both train and validation datasets). Depending on what kind of application you are building you will probably have different approaches while working with this data so let’s take an example case: you are trying to build an application that can accurately predict if a certain passage relates or not with an associated question/answer pair in terms of semantics content or meaning conveyed by both entities regardless their exact lexical meaning matches (i. e., check whether passage answers question ). In such case could opt for using supervised learning methods whose labels comes from predicting whether a passage correctly answers question. With such process one should first divide each row into two entities (containing only one) - i .e., remove sentences belonging only either one column or another - so that model can learn general distribution about both correlated concepts separately then ultimately find relationships between them when combined again into same sentence structure format than original row given earlier by dataset. With this approach one can also look towards recurrent neural networks which try making underlying semantics inference from sequences generally applied via natural language processing tasks like MT given its enormously complexity due usability large computation resources needed perform predictive transitions between various steps layer stack architecture mechanism deep learning has become plenty common while approaching problems within natural language processing context because tool’s ability represent Hidden knowledge pieces inside say longer tone texts through it power output those values representing probability distributions opposite language automatically found data just setting up basic Neural Network layer progression few lines code already being plenty

Research Ideas

Training machines to develop the ability to recognize logical patterns in questions and accurately provide answers.

Building automated question-answering systems for education or business purposes, by using the dataset as a training model which can be further refined with more complete data.

Creating interactive tutorials that generate logical questions related to the topic, assisted by correct answers and further explanation of it contained in the passages accompanying each item in this dataset

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: validation.csv

Column name	Description
question	A question related to logical thinking. (String)
answer	The answer to the question. (String)
passage	A related passage to the question. (String)

File: train.csv

Column name	Description
question	A question related to logical thinking. (String)
answer	The answer to the question. (String)
passage	A related passage to the question. (String)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit Huggingface Hub.

Tables

Train

@kaggle.thedevastator_unlock_logical_thinking_with_the_boolq_dataset.train

3.54 MB
9427 rows
3 columns


CREATE TABLE train (
  "question" VARCHAR,
  "answer" BOOLEAN,
  "passage" VARCHAR
);

Validation

@kaggle.thedevastator_unlock_logical_thinking_with_the_boolq_dataset.validation

1.18 MB
3270 rows
3 columns


CREATE TABLE validation (
  "question" VARCHAR,
  "answer" BOOLEAN,
  "passage" VARCHAR
);