RRepoGEO

REPOGEO REPORT · LITE

QData/TextAttack

Default branch master · commit 7f4a9930 · scanned 5/23/2026, 9:31:49 AM

GitHub: 3,423 stars · 447 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
93 /100
Healthy
Category recall
2 / 2
Avg rank #1.0 when recommended
Rule findings
2 pass · 0 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 QData/TextAttack, 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
  • mediumtopics#1
    Add 'robustness' to topics

    Why:

    CURRENT
    adversarial-attacks, adversarial-examples, adversarial-machine-learning, data-augmentation, machine-learning, natural-language-processing, nlp, security
    COPY-PASTE FIX
    adversarial-attacks, adversarial-examples, adversarial-machine-learning, data-augmentation, machine-learning, natural-language-processing, nlp, security, robustness
  • mediumreadme#2
    Add a differentiator statement to the 'About' section

    Why:

    CURRENT
    TextAttack is a Python framework for adversarial attacks, data augmentation, and model training in NLP.
    COPY-PASTE FIX
    TextAttack is a unified, modular, and extensible Python framework for adversarial attacks, data augmentation, and model training in NLP.
  • lowreadme#3
    Briefly clarify the 'model training' aspect in 'Why TextAttack?'

    Why:

    CURRENT
    4. **Train NLP models** using just a single command (all downloads included!)
    COPY-PASTE FIX
    4. **Train robust NLP models** using augmented data or adversarial training with just a single command (all downloads included!)

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
2 / 2
100% of queries surface QData/TextAttack
Avg rank
#1.0
Lower is better. #1 = top recommendation.
Share of voice
18%
Of all named tools, what % are you?
Top rival
OpenAttack
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAttack · recommended 1×
  2. Adversarial Robustness Toolbox (ART) · recommended 1×
  3. DeepRobust · recommended 1×
  4. Foolbox · recommended 1×
  5. Augly · recommended 1×
  • CATEGORY QUERY
    How can I generate adversarial examples to test my NLP model's robustness?
    you: #1
    AI recommended (in order):
    1. TextAttack ← you
    2. OpenAttack
    3. Adversarial Robustness Toolbox (ART)
    4. DeepRobust
    5. Foolbox
    Show full AI answer
  • CATEGORY QUERY
    What Python tools exist for text data augmentation to improve NLP model security?
    you: #1
    AI recommended (in order):
    1. TextAttack ← you
    2. Augly
    3. NLPAug
    4. Transformers (Hugging Face)
    5. NLTK (Natural Language Toolkit)
    6. SpaCy
    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 QData/TextAttack?
    pass
    AI named QData/TextAttack explicitly

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

  • If a team adopts QData/TextAttack in production, what risks or prerequisites should they evaluate first?
    pass
    AI named QData/TextAttack 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 QData/TextAttack solve, and who is the primary audience?
    pass
    AI named QData/TextAttack explicitly

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

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QData/TextAttack — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite