ProCreations/Mistake-To-Meaning

Clear Spelling Dataset

Overview

The Mistake to Meaning (M2M) dataset is a carefully crafted synthetic collection of 100,000 unique English spelling mistakes and their correct forms, intended for training high-quality typo correction and spell checking AI models. It covers various types of common mistakes observed frequently in real-world scenarios, such as:

• Keyboard adjacency typos
• Letter swaps and omissions
• Duplicate characters
• Phonetic substitution errors
• Commonly confused homophones (e.g., "their" vs. "there")

Dataset Format

The dataset is provided in CSV format with two clearly defined columns:

Column	Description	Example
error	The misspelled or incorrect word or phrase	"teh"
correct	The correct word or intended phrase	"the"

Usage

This dataset is ideal for:

• Training and fine-tuning typo correction models
• Benchmarking spell-checking algorithms
• Enhancing NLP model robustness to real-world noisy input

Quality Assurance

• No duplicates: Each (error, correct) pair is unique.
• Hand-curated seed set: Includes hundreds of common misspellings verified against real-world usage patterns.
• Realistic noise generation: Uses realistic error transformations mimicking genuine human typing behavior.

License (MIT)

This dataset is released under the permissive MIT License, which allows commercial and non-commercial use, distribution, and modification. Attribution is required:

Citation

If you use this dataset in your research or projects, please provide attribution similar to:

This [your project type] uses the Mistake to Learning dataset by ProCreations.

Enjoy training your typo-correction models!