metadata

dataset_name: grammar-correction
description: >
  A refined subset of the liweili/c4_200m dataset, derived from Google's C4_200M
  Synthetic Dataset for Grammatical Error Correction.  Contains sentence pairs
  where the input is ungrammatical and the output is grammatical, suitable for
  training grammatical error correction (GEC) models.
train_size: 100000
validation_size: 25000
data_fields:
  - input: Ungrammatical sentence (string)
  - output: Corrected, grammatical sentence (string)
intended_uses: >
  Designed for training and evaluating GEC models to automatically identify and
  correct grammatical errors in text.
limitations:
  - Synthetic data may not fully capture real-world grammatical errors.
  - Filtering may introduce biases based on classifier performance.
  - >-
    Focused on English grammar, limiting applicability to other languages or
    dialects.
task_categories:
  - text2text-generation
  - text-classification
language:
  - en
tags:
  - gec
  - grammar

grammar-correction

Dataset Summary

The grammar-correction dataset is a refined subset of the liweili/c4_200m dataset, derived from Google's C4_200M Synthetic Dataset for Grammatical Error Correction. It contains sentence pairs where the input is ungrammatical and the output is grammatical, making it suitable for training grammatical error correction (GEC) models.

Dataset Structure

Train set: 100 000 entries
Validation set: 25 000 entries

Data Fields

input: Ungrammatical sentence (string)
output: Corrected, grammatical sentence (string)

Dataset Creation

Source Data

This dataset is based on the liweili/c4_200m dataset, which includes 185 million sentence pairs generated from a cleaned English corpus.

Filtering Methodology

Entries were filtered using the agentlans/snowflake-arctic-xs-grammar-classifier to retain only those with ungrammatical inputs and grammatical outputs, enhancing the quality of training data for GEC tasks.

Considerations for Using the Data

Intended Uses

Designed for training and evaluating GEC models to automatically identify and correct grammatical errors in text.

Limitations and Biases

The dataset is synthetic and may not fully capture real-world grammatical errors.
Filtering may introduce biases based on classifier performance.
Focused on English grammar, limiting applicability to other languages or dialects.

Additional Information

Dataset Curators

Curated by filtering the liweili/c4_200m dataset, based on Google's C4_200M dataset.
Please check the licenses of those datasets for usage rights.
Also acknowledge the use of the agentlans/snowflake-arctic-xs-grammar-classifier for filtering.

Contributions

This dataset aims to provide high-quality resources for GEC model training and evaluation, building on the efforts of Google Research and the Hugging Face community.