RRepoGEO

REPOGEO REPORT · LITE

GEM-benchmark/NL-Augmenter

Default branch main · commit b64a8efe · scanned 6/3/2026, 5:57:43 PM

GitHub: 787 stars · 197 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface GEM-benchmark/NL-Augmenter, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    natural-language-processing, nlp, data-augmentation, text-augmentation, machine-learning, deep-learning, robustness, evaluation, benchmark
  • highreadme#2
    Strengthen README opening to emphasize NLP model robustness and evaluation

    Why:

    CURRENT
    The NL-Augmenter is a collaborative effort intended to add transformations of datasets dealing with natural language. Transformations augment text datasets in diverse ways, including: randomizing names and numbers, changing style/syntax, paraphrasing, KB-based paraphrasing ... and whatever creative augmentation you contribute. We invite submissions of transformations to this framework by way of GitHub pull request.
    COPY-PASTE FIX
    The NL-Augmenter is a collaborative framework for natural language data augmentation, designed to improve the robustness and evaluate the weaknesses of NLP models. It enables diverse transformations of text datasets, including randomizing names and numbers, changing style/syntax, paraphrasing, and KB-based paraphrasing. We invite submissions of creative augmentations to this framework by way of GitHub pull request.
  • mediumhomepage#3
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    https://[YOUR_PROJECT_HOMEPAGE_OR_PAPER_URL]

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface GEM-benchmark/NL-Augmenter
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NLPAug
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. NLPAug · recommended 2×
  2. TextAttack · recommended 2×
  3. NLTK · recommended 2×
  4. spaCy · recommended 2×
  5. AugLy · recommended 1×
  • CATEGORY QUERY
    What tools help augment text datasets with diverse natural language transformations for NLP?
    you: not recommended
    AI recommended (in order):
    1. NLPAug
    2. TextAttack
    3. AugLy
    4. Hugging Face datasets library
    5. Easy Data Augmentation (EDA)
    6. Google's text_data_augmentation (TensorFlow Addons)
    7. NLTK
    8. spaCy
    9. Pattern

    AI recommended 9 alternatives but never named GEM-benchmark/NL-Augmenter. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I programmatically generate varied text examples to improve NLP model robustness?
    you: not recommended
    AI recommended (in order):
    1. TextAttack
    2. NLPAug
    3. Hugging Face Transformers
    4. spaCy
    5. NLTK

    AI recommended 5 alternatives but never named GEM-benchmark/NL-Augmenter. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of GEM-benchmark/NL-Augmenter?
    pass
    AI named GEM-benchmark/NL-Augmenter explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts GEM-benchmark/NL-Augmenter in production, what risks or prerequisites should they evaluate first?
    pass
    AI named GEM-benchmark/NL-Augmenter explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo GEM-benchmark/NL-Augmenter solve, and who is the primary audience?
    pass
    AI named GEM-benchmark/NL-Augmenter explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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GEM-benchmark/NL-Augmenter — RepoGEO report