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---
license: mit
task_categories:
- text-generation
language:
- en
tags:
- typo
pretty_name: M2M
size_categories:
- 10K<n<100K
---
# 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! |