RRepoGEO

REPOGEO REPORT · LITE

shibing624/pycorrector

Default branch master · commit 7e3caeaf · scanned 6/27/2026, 6:43:03 AM

GitHub: 6,474 stars · 1,160 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
67 /100
Needs work
Category recall
1 / 2
Avg rank #1.0 when recommended
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 shibing624/pycorrector, 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
    Clarify README's opening statement for AI-powered application use

    Why:

    CURRENT
    **pycorrector**: 中文文本纠错工具。支持中文音似、形似、语法错误纠正，python3.8开发。
    COPY-PASTE FIX
    **pycorrector** is a comprehensive Python toolkit for Chinese text error correction, designed for developers building AI-powered applications. It supports correcting phonetic, shape, and grammatical errors, integrating state-of-the-art models like Kenlm, T5, MacBERT, ChatGLM3, and Qwen2.5 for out-of-the-box use in various correction scenarios.
  • mediumtopics#2
    Add broader AI/NLP and explicit grammar correction topics

    Why:

    CURRENT
    csc, error-correction, error-detection, kenlm, macbert4csc, pycorrector, spelling-errors, t5
    COPY-PASTE FIX
    csc, error-correction, error-detection, kenlm, macbert4csc, pycorrector, spelling-errors, t5, nlp, ai, deep-learning, grammar-correction, chinese-nlp
  • lowreadme#3
    Add a clear statement about the core differentiator

    Why:

    COPY-PASTE FIX
    Add this sentence to the 'Introduction' section, after the existing paragraph ending '本项目重点解决其中的"音似、形字、语法、专名错误"等类型。':

"Unlike single-model solutions, pycorrector offers a comprehensive, multi-strategy approach to Chinese text error correction. It integrates both traditional linguistic methods (e.g., Pinyin, strokes, confusion sets) and modern deep learning models (e.g., BERT, ERNIE, GPT, LLaMA2, Qwen), providing flexibility and optimized performance across diverse error types and application scenarios."

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
1 / 2
50% of queries surface shibing624/pycorrector
Avg rank
#1.0
Lower is better. #1 = top recommendation.
Share of voice
9%
Of all named tools, what % are you?
Top rival
FASPell
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. FASPell · recommended 1×
  2. HanLP · recommended 1×
  3. Jieba · recommended 1×
  4. pypinyin · recommended 1×
  5. Baidu AI Open Platform · recommended 1×
  • CATEGORY QUERY
    What's a good Python library for correcting common errors in Chinese text?
    you: #1
    AI recommended (in order):
    1. pycorrector ← you
    2. FASPell
    3. HanLP
    4. Jieba
    5. pypinyin
    Show full AI answer
  • CATEGORY QUERY
    How can I implement AI-powered Chinese grammar and spelling correction in my application?
    you: not recommended
    AI recommended (in order):
    1. Baidu AI Open Platform
    2. Tencent Cloud NLP
    3. Alibaba Cloud NLP
    4. LanguageTool
    5. Microsoft Azure Cognitive Services
    6. Google Cloud Natural Language API

    AI recommended 6 alternatives but never named shibing624/pycorrector. 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 shibing624/pycorrector?
    pass
    AI did not name shibing624/pycorrector — 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 shibing624/pycorrector in production, what risks or prerequisites should they evaluate first?
    pass
    AI named shibing624/pycorrector 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 shibing624/pycorrector solve, and who is the primary audience?
    pass
    AI named shibing624/pycorrector explicitly

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

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shibing624/pycorrector — 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