# 425776024/nlpcda

一键中文数据增强包 ； NLP数据增强、bert数据增强、EDA：pip install nlpcda

Repository: https://github.com/425776024/nlpcda
Canonical: https://ross.abutalabs.com/products/nlpcda
Language: Python
License: Apache-2.0
License Family: permissive
Topics: nlp, data-augmentation, chinese-data-augmentation, nlpcda, chinese-eda
Last push: 2025-03-18T08:26:13+00:00

## Health v2 (maintenance only)
Score: 28/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 12, release rhythm 8, longevity 100
- inputs: {"age_days": 2439, "days_push": 533, "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 1878, forks 171 (observed 2026-08-28T04:05:48.398826+00:00)

## What it is
nlpcda is a Python library for one-click Chinese NLP data augmentation. It provides techniques like random entity replacement, synonym substitution, homophone character swaps, character deletion, neighbor permutation, BIO-format NER augmentation, translation-based augmentation, and SimBERT-based similar sentence generation.

## Use cases
- augment chinese text training data for nlp models
- generate similar sentences with bert for data augmentation
- augment NER BIO labeled data
- improve model robustness against adversarial text perturbations
- synonym replacement for chinese corpus expansion
- create more training examples from a small chinese dataset

## When to choose
- you need Chinese-language text augmentation with semantic preservation
- you want multiple augmentation strategies (EDA, NER-aware, SimBERT) in one pip-installable package
- you are training BERT-style Chinese models and need more varied training data

## When to avoid
- you need data augmentation for non-Chinese languages
- you expect accuracy gains in pure leaderboard-score competitions without semantic variation
- you need production text processing rather than training-data generation

## Facets
- artifact type: library
- maturity: active
- function: nlp, data-generation, machine-learning
- domain: machine-learning, data-science
- platform: python, cross-platform
- tags: data-augmentation, chinese-nlp, eda, bert, ner, text-generation, simbert, natural-language-processing

## Member repositories
- 425776024/nlpcda (main) score 28

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:48.398826+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 2026-08-30T03:14:38.698752+00:00, confidence not recorded.
  - readme: https://github.com/425776024/nlpcda (fetched 2026-08-28T04:05:48.398826+00:00, sha 1ceb0d8b6e5b)
  - registry_pypi: https://pypi.org/pypi/nlpcda/json (fetched 2026-08-29T10:53:21.250287+00:00, sha 8a95772d262d)
- Data as of 2026-08-30T08:39:29.467469+00:00.