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teh
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the
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receive
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occured
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occurred
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seperate
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separate
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definately
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definitely
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wierd
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weird
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goverment
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government
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enviroment
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environment
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becuase
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because
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untill
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adress
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tommorow
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tomorrow
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accomodate
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maintainance
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restaraunt
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restaurant
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twelth
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twelfth
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vaccum
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occurence
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occurrence
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against
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artic
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athiest
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calender
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calendar
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Febuary
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firey
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grammer
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grammar
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heirarchy
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hierarchy
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humerous
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independent
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jewelery
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jewelry
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knowlege
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knowledge
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liason
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liaison
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millenium
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millennium
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miniscule
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morgage
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mortgage
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noticeing
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occuring
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pasttime
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pastime
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posession
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possession
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publically
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publicly
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receipt
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tommorrow
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there
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their
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your
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you're
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your
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its
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tecnolgoy
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goo morning
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community
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as soon ppossible
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as soon as possible
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stategy
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strategy
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tomorw
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End of preview. Expand
in Data Studio
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!
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