RRepoGEO

REPOGEO REPORT · LITE

MorvanZhou/NLP-Tutorials

Default branch master · commit 3aa02a13 · scanned 6/6/2026, 1:17:47 PM

GitHub: 951 stars · 314 forks

AI VISIBILITY SCORE
33 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 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 MorvanZhou/NLP-Tutorials, 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 paragraph to emphasize its tutorial nature for learners

    Why:

    CURRENT
    # Natural Language Processing Tutorial

Tutorial in Chinese can be found in mofanpy.com.

This repo includes many simple implementations of models in Neural Language Processing (NLP).
    COPY-PASTE FIX
    # Natural Language Processing Tutorial: Simple Python Implementations for Learning

This repository offers beginner-friendly, simple Python implementations of core Natural Language Processing (NLP) models, designed as a hands-on tutorial resource for students and learners. Full tutorials in Chinese are available on mofanpy.com.
  • mediumabout#2
    Clarify the 'About' description to highlight its educational purpose

    Why:

    CURRENT
    Simple implementations of NLP models. Tutorials are written in Chinese on my website https://mofanpy.com
    COPY-PASTE FIX
    Beginner-friendly Python implementations of core NLP models, serving as a hands-on tutorial for learning. Full Chinese tutorials are available on mofanpy.com.
  • mediumtopics#3
    Add more specific topics related to learning and education

    Why:

    CURRENT
    attention, bert, elmo, gpt, nlp, seq2seq, transformer, tutorial, w2v
    COPY-PASTE FIX
    attention, bert, elmo, gpt, nlp, seq2seq, transformer, tutorial, w2v, nlp-for-beginners, deep-learning-tutorials, educational-resource, python-tutorials

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 MorvanZhou/NLP-Tutorials
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NLTK
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NLTK · recommended 1×
  2. spaCy · recommended 1×
  3. scikit-learn · recommended 1×
  4. Gensim · recommended 1×
  5. Hugging Face Transformers · recommended 1×
  • CATEGORY QUERY
    Looking for simple Python code examples to understand core natural language processing models.
    you: not recommended
    AI recommended (in order):
    1. NLTK
    2. spaCy
    3. scikit-learn
    4. Gensim
    5. Hugging Face Transformers
    6. TextBlob

    AI recommended 6 alternatives but never named MorvanZhou/NLP-Tutorials. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I find Python implementations for advanced NLP models like Transformers and BERT?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. PyTorch-Transformers
    3. TensorFlow Model Garden (tensorflow/models)
    4. Keras NLP (keras-team/keras-nlp)
    5. AllenNLP (allenai/allennlp)
    6. DeepPavlov (deepmipt/DeepPavlov)
    7. spaCy (explosion/spaCy)

    AI recommended 7 alternatives but never named MorvanZhou/NLP-Tutorials. This is the gap to close.

    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 MorvanZhou/NLP-Tutorials?
    pass
    AI did not name MorvanZhou/NLP-Tutorials — likely talking about a different project

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

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

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

Embed your GEO score

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MARKDOWN (README)
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MorvanZhou/NLP-Tutorials — 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