preligens-lab/textnoisr
Adding random noise to a text dataset, and controlling very accurately the quality of the result
When you're working with text data, it's often useful to create slightly altered versions of your text to test how robust your analysis or model is. This tool takes your original text data and introduces controlled, random errors like typos (inserting, deleting, substituting, or swapping characters). It's designed for anyone who needs to generate realistic noisy text for testing or data augmentation, ensuring the output closely matches a target level of 'noisiness'.
Available on PyPI.
Use this if you need to create a text dataset with a very specific, quantifiable level of character-level noise for training or evaluating text analysis systems.
Not ideal if you need to introduce noise at a word or sentence level, or if you only need very simple, uncontrolled random text alterations.
Stars
20
Forks
3
Language
Python
License
BSD-2-Clause
Category
Last pushed
Mar 14, 2026
Commits (30d)
0
Dependencies
3
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/preligens-lab/textnoisr"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related tools
google/langfun
OO for LLMs
tanaos/artifex
Small Language Model Inference, Fine-Tuning and Observability. No GPU, no labeled data needed.
vulnerability-lookup/VulnTrain
A tool to generate datasets and models based on vulnerabilities descriptions from @Vulnerability-Lookup.
masakhane-io/masakhane-mt
Machine Translation for Africa
DataScienceUIBK/HintEval
HintEvalš”: A Comprehensive Framework for Hint Generation and Evaluation for Questions