# makcedward/nlpaug

Data augmentation for NLP

Repository: https://github.com/makcedward/nlpaug
Canonical: https://ross.abutalabs.com/products/nlpaug
Homepage: https://makcedward.github.io/
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: nlp, augmentation, machine-learning, artificial-intelligence, data-science, natural-language-processing, adversarial-attacks, adversarial-example, ai, ml
Last push: 2026-08-09T05:05:17+00:00

## Health v2 (maintenance only)
Score: 66/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 96, release rhythm 8, longevity 100
- inputs: {"age_days": 2722, "days_push": 24, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4665, forks 473 (observed 2026-08-28T04:08:56.518746+00:00)

## What it is
nlpaug is a Python library for augmenting textual, audio, and spectrogram data to improve machine learning model performance. It provides Augmenter components and a Flow pipeline for composing multiple augmentation techniques.

## Use cases
- augment text data for training NLP models
- generate synthetic training examples when labeled data is scarce
- augment audio and spectrogram data for speech models
- create adversarial examples to test model robustness
- build augmentation pipelines for deep learning workflows

## When to choose
- you need lightweight, plug-and-play data augmentation for NLP or audio in Python
- you want to boost model performance without manually collecting more labeled data
- you need multilingual text augmentation or adversarial example generation

## When to avoid
- you need image or general computer-vision augmentation
- you require a non-Python environment
- you need production-scale distributed data generation rather than in-process augmentation

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, nlp, audio-processing, data-generation
- domain: machine-learning, data-science
- platform: python, cross-platform
- tags: data-augmentation, adversarial-examples, text-augmentation, spectrogram, deep-learning, natural-language-processing, audio

## Member repositories
- makcedward/nlpaug (main) score 66

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:56.518746+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:19:25.209403+00:00, confidence not recorded.
  - readme: https://github.com/makcedward/nlpaug (fetched 2026-08-28T04:08:56.518746+00:00, sha 0dddcfc3f157)
  - homepage: https://makcedward.github.io/ (fetched 2026-08-29T09:03:43.838885+00:00, sha b8806abb5f24)
  - registry_pypi: https://pypi.org/pypi/nlpaug/json (fetched 2026-08-29T09:03:43.841303+00:00, sha c4ff299afa38)
- Data as of 2026-08-30T08:39:29.467469+00:00.