RRepoGEO

REPOGEO REPORT · LITE

makcedward/nlp

Default branch master · commit 2f12277b · scanned 5/11/2026, 4:43:08 PM

GitHub: 1,081 stars · 322 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 makcedward/nlp, 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
  • highreadme#1
    Reposition the README's opening to clearly state its purpose as a learning hub

    Why:

    CURRENT
    # NLP - Tutorial
Repository to show how NLP can tacke real problem. Including the source code, dataset, state-of-the art in NLP
    COPY-PASTE FIX
    # NLP - Tutorial
This repository serves as a comprehensive learning hub, providing practical tutorials, source code, datasets, and insights into state-of-the-art techniques for Natural Language Processing.
  • highlicense#2
    Add a LICENSE file to the repository root

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with the text of the MIT License.
  • mediumtopics#3
    Add more specific topics to reflect its nature as a tutorial/resource hub

    Why:

    CURRENT
    ai, data-science, deep-learning, machine-learning, nlp
    COPY-PASTE FIX
    ai, data-science, deep-learning, machine-learning, nlp, nlp-tutorials, nlp-examples, nlp-resources, learning-nlp

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 makcedward/nlp
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 1×
  2. fastai/fastai · recommended 1×
  3. nltk/nltk · recommended 1×
  4. Stanford CS224N · recommended 1×
  5. explosion/spaCy · recommended 1×
  • CATEGORY QUERY
    Where can I find comprehensive tutorials and examples for learning natural language processing?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. fast.ai (fastai/fastai)
    3. NLTK (nltk/nltk)
    4. Stanford CS224N
    5. spaCy (explosion/spaCy)
    6. PyTorch (pytorch/pytorch)
    7. TensorFlow (tensorflow/tensorflow)

    AI recommended 7 alternatives but never named makcedward/nlp. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective techniques and libraries for text data augmentation in NLP models?
    you: not recommended
    AI recommended (in order):
    1. textattack
    2. nltk
    3. Hugging Face Transformers
    4. googletrans
    5. OpenAI API
    6. spaCy

    AI recommended 6 alternatives but never named makcedward/nlp. 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 makcedward/nlp?
    pass
    AI named makcedward/nlp explicitly

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

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

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

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makcedward/nlp — 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