RRepoGEO

REPOGEO REPORT · LITE

PKU-TANGENT/nlp-tutorial

Default branch main · commit b2d8768a · scanned 5/13/2026, 5:42:52 AM

GitHub: 1,441 stars · 130 forks

AI VISIBILITY SCORE
28 /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
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 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

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • hightopics#1
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    nlp, natural-language-processing, tutorial, deep-learning, machine-learning, python, education, learning, beginner, pku, tangent, transformers, huggingface
  • highreadme#2
    Clarify README's opening statement to emphasize comprehensive tutorial nature

    Why:

    CURRENT
    # PKU-TANGENT nlp-tutorial

本教程供新加入 TANGENT 实验室的同学入门 NLP 使用
    COPY-PASTE FIX
    # PKU-TANGENT nlp-tutorial: A Comprehensive Guide for NLP Beginners

本教程旨在为自然语言处理（NLP）初学者提供一个全面的入门指南，特别适合希望系统学习NLP基础知识、深度学习应用及动手实践的同学。
  • mediumabout#3
    Expand repository description with key content areas

    Why:

    CURRENT
    NLP新手入门教程
    COPY-PASTE FIX
    为NLP初学者提供全面的入门教程，涵盖机器学习、深度学习基础、文献阅读指导，以及基于CNN、RNN、Transformer和Hugging Face的文本分类、命名实体识别、机器翻译等实践任务。

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 Transformers library
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers library · recommended 1×
  2. Hugging Face Transformers · recommended 1×
  3. Keras · recommended 1×
  4. spaCy · recommended 1×
  5. PyTorch · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive guide to start learning natural language processing?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers library

    AI recommended 1 alternative but never named PKU-TANGENT/nlp-tutorial. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for practical deep learning examples to implement common natural language processing tasks in Python.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Keras
    3. spaCy
    4. PyTorch
    5. Gensim

    AI recommended 5 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 did not name PKU-TANGENT/nlp-tutorial — 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 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

Drop this badge into the README of PKU-TANGENT/nlp-tutorial. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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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
PKU-TANGENT/nlp-tutorial — RepoGEO report