--- license: mit task_categories: - text-classification - text-generation tags: - nlp - text - text classification - text generation - error - error correction - synthetic - generated - ocr - text-correction - code-correction --- [![Website](https://img.shields.io/badge/webXOS.netlify.app-Explore_Apps-00d4aa?style=for-the-badge&logo=netlify&logoColor=white)](https://webxos.netlify.app) [![GitHub](https://img.shields.io/badge/GitHub-webxos/webxos-181717?style=for-the-badge&logo=github&logoColor=white)](https://github.com/webxos/webxos) [![Hugging Face](https://img.shields.io/badge/Hugging_Face-🤗_webxos-FFD21E?style=for-the-badge&logo=huggingface&logoColor=white)](https://huggingface.co/webxos) [![Follow on X](https://img.shields.io/badge/Follow_@webxos-1DA1F2?style=for-the-badge&logo=x&logoColor=white)](https://x.com/webxos)
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# Synthetic Error Correction *UNDER DEVELOPMENT* Small synthetic dataset (28 examples: 22 train, 6 test) of error-correction pairs for training text correction models (e.g., OCR post-processing, typos). ## Dataset Summary This dataset was generated locally using the TYPE/RIGHTER offline-first PWA. It contains synthetic error-correction pairs for training text correction models. *Generated by TYPE/RIGHTER • Offline-First PWA, downloadable in the /typerighter/ folder of the repo* ## Supported Tasks and Leaderboards - Text error correction - OCR post-processing - Typographical error correction ## Languages English ## Dataset Structure ### Data Instances Each instance contains: - `original`: The text as it appeared in the uploaded file - `corrupted`: The procedurally "damaged" version for error detection training - `target`: The cleaned, normalized version for error correction training - `tags`: Content-based classification tags - `source`: Original file name ### Data Fields - `original`: string - Original text - `corrupted`: string - Text with injected errors - `target`: string - Cleaned target text - `tags`: Sequence[string] - Content classification tags - `source`: string - Source file name ### Data Splits - Train: 22 examples - Test: 6 examples ## Dataset Creation ### Curation Rationale This dataset was created to provide synthetic data for training error correction models from user-provided documents. ### Source Data The data was generated from user-uploaded documents in .txt, .csv, .md, or .jsonl formats. ### Annotations Annotations were automatically generated through: 1. Text normalization (simulated cleaning) 2. Controlled error injection based on configurable parameters 3. Content-based classification ### Personal and Sensitive Information All processing occurs locally in the browser. No data was uploaded to any server. ## Considerations for Using the Data ### Social Impact of Dataset This dataset enables the creation of error correction models for various text domains. ### Discussion of Biases The error patterns are synthetic and may not perfectly represent real-world error distributions. ### Other Known Limitations - Synthetic errors may not capture all real-world error patterns - Limited to text-based errors (no formatting or layout errors) ## Additional Information ### Dataset Curators Generated by TYPE/RIGHTER PWA by webXOS ### Licensing Information MIT ### Citation Information ``` @misc{typerighter2026, title={TYPE/RIGHTER Synthetic Error Correction Dataset}, author={TYPE/RIGHTER PWA}, year={2026}, url={https://github.com/typerighter} } ``` ### Contributions Thanks to the user who generated this dataset using TYPE/RIGHTER. ## Error Distribution - Keyboard Adjacent Errors: 20% - Case Corruption: 15% - Symbol Insertion: 10% - OCR Line Noise: 10% ## Generated on 2026-01-07T01:04:58.601Z --- *Generated by TYPE/RIGHTER • Offline-First PWA • Privacy by Design • webXOS 2026*