RRepoGEO

REPOGEO REPORT · LITE

PKU-TANGENT/nlp-tutorial

Default branch main · commit b2d8768a · scanned 6/23/2026, 2:28:31 PM

GitHub: 1,446 stars · 128 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 PKU-TANGENT/nlp-tutorial, 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

2 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 sentence for broader appeal

    Why:

    CURRENT
    本教程供新加入 TANGENT 实验室的同学入门 NLP 使用
    COPY-PASTE FIX
    本教程旨在为自然语言处理（NLP）初学者提供系统性的入门指导，尤其适合新加入 TANGENT 实验室的同学。它涵盖了从基础理论到动手实践的全面内容。
  • mediumabout#2
    Expand the repository description to highlight hands-on practice

    Why:

    CURRENT
    NLP新手入门教程
    COPY-PASTE FIX
    面向NLP初学者的系统性教程，涵盖基础理论、文献阅读与深度学习模型（如文本分类、NER、NMT、Transformer）的动手实践。

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 PKU-TANGENT/nlp-tutorial
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face's NLP Course
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face's NLP Course · recommended 1×
  2. NLTK Book (Natural Language Processing with Python) · recommended 1×
  3. spaCy 101: Everything you need to know · recommended 1×
  4. explosion/spaCy · recommended 1×
  5. Google's Machine Learning Crash Course - Text Classification · recommended 1×
  • CATEGORY QUERY
    I'm new to natural language processing; what's a good beginner tutorial for core concepts?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face's NLP Course
    2. NLTK Book (Natural Language Processing with Python)
    3. spaCy 101: Everything you need to know
    4. spaCy (explosion/spaCy)
    5. Google's Machine Learning Crash Course - Text Classification
    6. fast.ai's Practical Deep Learning for Coders (NLP sections)

    AI recommended 6 alternatives but never named PKU-TANGENT/nlp-tutorial. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for hands-on examples to build deep learning models for common NLP applications.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library (huggingface/transformers)
    2. PyTorch
    3. Keras
    4. TensorFlow
    5. fast.ai
    6. NLTK

    AI recommended 6 alternatives but never named PKU-TANGENT/nlp-tutorial. 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 PKU-TANGENT/nlp-tutorial?
    pass
    AI named PKU-TANGENT/nlp-tutorial explicitly

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

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