# jasonwei20/eda_nlp

Data augmentation for NLP, presented at EMNLP 2019

Repository: https://github.com/jasonwei20/eda_nlp
Canonical: https://ross.abutalabs.com/products/eda_nlp
Homepage: https://arxiv.org/abs/1901.11196
Language: Python
License Family: other
Topics: nlp, data-augmentation, text-classification, synonyms, embeddings, sentence, classification, rnn, cnn, swap, position
Last push: 2023-03-19T21:39:48+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2807, "days_push": 1263, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1652, forks 312 (observed 2026-08-28T04:05:17.007453+00:00)

## What it is
EDA (Easy Data Augmentation) is a Python library implementing four simple text augmentation operations—synonym replacement, random insertion, random swap, and random deletion—for boosting text classification performance. It is the official code for an EMNLP 2019 paper and works especially well on small training datasets.

## Use cases
- augment a small text classification dataset
- generate synthetic training sentences via synonym replacement
- improve classifier accuracy with limited labeled data
- apply random swap and deletion augmentation to NLP training data
- replicate EMNLP 2019 EDA paper experiments

## When to choose
- you have a small text classification dataset (under ~500 examples) and need more training data
- you want lightweight augmentation without training a language model
- you need a simple, well-cited baseline augmentation technique

## When to avoid
- you need augmentation for languages other than English without extra work
- you want context-aware or LLM-based paraphrasing quality
- you need a maintained, licensed production dependency (no license, minimal upkeep)

## Facets
- artifact type: library
- maturity: maintenance
- function: nlp, data-generation, machine-learning
- domain: machine-learning, data-science
- platform: python, cli
- tags: data-augmentation, text-classification, synonym-replacement, wordnet, nltk, research-code, emnlp-2019, natural-language-processing

## Member repositories
- jasonwei20/eda_nlp (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:17.007453+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 2026-08-30T03:45:00.350715+00:00, confidence not recorded.
  - readme: https://github.com/jasonwei20/eda_nlp (fetched 2026-08-28T04:05:17.007453+00:00, sha 12566a722c76)
  - homepage: https://arxiv.org/abs/1901.11196 (fetched 2026-08-29T11:18:13.706183+00:00, sha 0bbc7f7929f7)
  - site_page: https://info.arxiv.org/about/donate.html (fetched 2026-08-29T11:18:13.715552+00:00, sha cca9c3a11c56)
  - site_page: https://info.arxiv.org/about/ourmembers.html (fetched 2026-08-29T11:18:13.719194+00:00, sha 47cbc55ff1de)
  - site_page: https://info.arxiv.org/about (fetched 2026-08-29T11:18:13.721223+00:00, sha a1f16f915a9a)
  - site_page: https://info.arxiv.org/labs/index.html (fetched 2026-08-29T11:18:13.717440+00:00, sha b14a8d05a0ec)
- Data as of 2026-08-30T08:39:29.467469+00:00.