RRepoGEO

REPOGEO REPORT · LITE

yuanzhoulvpi2017/zero_nlp

Default branch main · commit 0404bc27 · scanned 6/25/2026, 3:07:59 PM

GitHub: 3,824 stars · 444 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
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 yuanzhoulvpi2017/zero_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
  • highreadme#1
    Explicitly state active maintenance and recent updates in README

    Why:

    COPY-PASTE FIX
    在README顶部，例如在`特点`部分之前，添加一行明确的声明：`本项目持续活跃更新，致力于提供最新的中文NLP解决方案，已支持Llama2、ChatGLM-6B、LLaVA等前沿大模型。`
  • highreadme#2
    Clarify the project's name/purpose in the README

    Why:

    CURRENT
    # zero to nlp
    COPY-PASTE FIX
    在`# zero to nlp`标题下方，添加一行解释：`本项目旨在提供从零开始（zero to nlp）构建中文NLP解决方案的完整框架，而非零样本学习（zero-shot learning）相关内容。`
  • mediumabout#3
    Add a homepage URL to the repository's 'About' section

    Why:

    COPY-PASTE FIX
    Set the homepage URL to `https://github.com/yuanzhoulvpi2017/zero_nlp` or a dedicated project documentation site if one exists.

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 yuanzhoulvpi2017/zero_nlp
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 2×
  2. PaddleNLP · recommended 1×
  3. ModelScope · recommended 1×
  4. OpenBMB · recommended 1×
  5. PyTorch-Lightning · recommended 1×
  • CATEGORY QUERY
    How to build comprehensive Chinese NLP solutions for large models using PyTorch?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PaddleNLP
    3. ModelScope
    4. OpenBMB
    5. PyTorch-Lightning
    6. FastText
    7. Jieba
    8. Spacy

    AI recommended 8 alternatives but never named yuanzhoulvpi2017/zero_nlp. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need a PyTorch framework for finetuning large Chinese language models with massive datasets.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. DeepSpeed
    3. PyTorch Lightning
    4. Megatron-LM
    5. FairSeq

    AI recommended 5 alternatives but never named yuanzhoulvpi2017/zero_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 yuanzhoulvpi2017/zero_nlp?
    pass
    AI named yuanzhoulvpi2017/zero_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 yuanzhoulvpi2017/zero_nlp in production, what risks or prerequisites should they evaluate first?
    pass
    AI named yuanzhoulvpi2017/zero_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 yuanzhoulvpi2017/zero_nlp solve, and who is the primary audience?
    pass
    AI named yuanzhoulvpi2017/zero_nlp 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 yuanzhoulvpi2017/zero_nlp. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/yuanzhoulvpi2017/zero_nlp.svg)](https://repogeo.com/en/r/yuanzhoulvpi2017/zero_nlp)
HTML
<a href="https://repogeo.com/en/r/yuanzhoulvpi2017/zero_nlp"><img src="https://repogeo.com/badge/yuanzhoulvpi2017/zero_nlp.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

yuanzhoulvpi2017/zero_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