README.md

metadata

dataset_info:
  features:
    - name: id
      dtype: string
    - name: title
      dtype: string
    - name: context
      dtype: string
    - name: question
      dtype: string
    - name: answers
      struct:
        - name: answer_start
          sequence: int32
        - name: text
          sequence: string
  splits:
    - name: train
      num_bytes: 116696879
      num_examples: 130316
    - name: validation
      num_bytes: 11660319
      num_examples: 11873
  download_size: 17698683
  dataset_size: 128357198
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
task_categories:
  - question-answering
language:
  - en
size_categories:
  - 100K<n<1M

Clean SQuAD Classic v2

This is a refined version of the SQuAD v2 dataset. It has been preprocessed to ensure higher data quality and usability for NLP tasks such as Question Answering.

Description

The Clean SQuAD Classic v2 dataset was created by applying preprocessing steps to the original SQuAD v2 dataset, including:

• Trimming whitespace: All leading and trailing spaces have been removed from the question field.
• Minimum question length: Questions with fewer than 12 characters were filtered out to remove overly short or uninformative entries.

Unlike the Clean SQuAD v2 dataset, this dataset does not contain a separate test split. It retains the classic two-way split of train and validation, following the traditional structure of the original SQuAD v2 dataset.

Dataset Structure

The dataset is divided into two subsets:

1. Train: The primary dataset for model training.
2. Validation: A dataset for hyperparameter tuning and model validation.

Data Fields

Each subset contains the following fields:

• id: Unique identifier for each question-context pair.
• title: Title of the article the context is derived from.
• context: Paragraph from which the answer is extracted.
• question: Preprocessed question string.
• answers: Dictionary containing:
  • text: The text of the correct answer(s), if available. Empty for unanswerable questions.
  • answer_start: Character-level start position of the answer in the context, if available.

Usage

The dataset is hosted on the Hugging Face Hub and can be loaded with the following code:

from datasets import load_dataset

dataset = load_dataset("decodingchris/clean_squad_classic_v2")