RRepoGEO

REPOGEO REPORT · LITE

makcedward/nlp

Default branch master · commit 2f12277b · scanned 6/21/2026, 9:42:53 PM

GitHub: 1,082 stars · 321 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
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
  • highlicense#1
    Add a LICENSE file to clarify usage rights

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root, for example, using the MIT License, to clearly state how others can use and contribute to your code examples.
  • highreadme#2
    Reposition the README's opening to emphasize its tutorial/learning nature

    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
    Change the first line of your README to something like: "# NLP - A Curated Collection of Practical NLP Tutorials and Code Examples" or "# NLP - My Journey and Practical Tutorials in Natural Language Processing".
  • mediumtopics#3
    Add more specific topics to highlight the repository's learning focus

    Why:

    CURRENT
    ai, data-science, deep-learning, machine-learning, nlp
    COPY-PASTE FIX
    Add `nlp-tutorials`, `learning-nlp`, `nlp-examples`, `data-science-tutorials` to the existing topics.

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
nltk/nltk
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. nltk/nltk · recommended 1×
  2. sloria/TextBlob · recommended 1×
  3. TextAttack/TextAttack · recommended 1×
  4. Google Cloud Translation API · recommended 1×
  5. DeepL API · recommended 1×
  • CATEGORY QUERY
    What are effective data augmentation strategies for improving text-based NLP models?
    you: not recommended
    AI recommended (in order):
    1. NLTK (nltk/nltk)
    2. TextBlob (sloria/TextBlob)
    3. textattack (TextAttack/TextAttack)
    4. Google Cloud Translation API
    5. DeepL API
    6. Microsoft Translator Text API
    7. Hugging Face Transformers (huggingface/transformers)
    8. BERT
    9. RoBERTa
    10. GPT-2
    11. PyTorch (pytorch/pytorch)
    12. TensorFlow (tensorflow/tensorflow)
    13. TextGAN
    14. SeqGAN
    15. torchtext (pytorch/text)
    16. nlu (JohnSnowLabs/nlu)

    AI recommended 16 alternatives but never named makcedward/nlp. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I preprocess text data efficiently for machine learning and deep learning tasks?
    you: not recommended
    AI recommended (in order):
    1. spaCy
    2. NLTK (Natural Language Toolkit)
    3. Hugging Face Transformers (tokenizers library)
    4. scikit-learn (text feature extraction modules)
    5. Gensim
    6. TextBlob

    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