RRepoGEO

REPOGEO REPORT · LITE

Y1ran/NLP-BERT--ChineseVersion

Default branch master · commit 57d13e86 · scanned 6/2/2026, 8:42:51 AM

GitHub: 840 stars · 195 forks

AI VISIBILITY SCORE
17 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
1 / 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 Y1ran/NLP-BERT--ChineseVersion, 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
  • highabout#1
    Update the 'About' description to explicitly mention Chinese NLP

    Why:

    CURRENT
    谷歌自然语言处理模型BERT：论文解析与python代码
    COPY-PASTE FIX
    A PyTorch implementation and detailed analysis of Google's BERT model, specifically tailored for Chinese Natural Language Processing (NLP).
  • highlicense#2
    Create a LICENSE file in the repository root

    Why:

    CURRENT
    License: (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root containing the full text of the Apache License 2.0.

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 Y1ran/NLP-BERT--ChineseVersion
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 1×
  2. PaddlePaddle/PaddleNLP · recommended 1×
  3. OpenNMT/OpenNMT-py · recommended 1×
  4. kmkurn/pytorch-crf · recommended 1×
  5. fxsjy/jieba · recommended 1×
  • CATEGORY QUERY
    How can I implement deep learning models for Chinese natural language processing using PyTorch?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. PaddleNLP (PaddlePaddle/PaddleNLP)
    3. OpenNMT-py (OpenNMT/OpenNMT-py)
    4. pytorch-crf (kmkurn/pytorch-crf)
    5. Jieba (fxsjy/jieba)
    6. Stanza (stanfordnlp/stanza)

    AI recommended 6 alternatives but never named Y1ran/NLP-BERT--ChineseVersion. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking Python tools to build and understand large-scale natural language processing models.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch Lightning
    3. spaCy
    4. TensorFlow
    5. AllenNLP
    6. NLTK (Natural Language Toolkit)

    AI recommended 6 alternatives but never named Y1ran/NLP-BERT--ChineseVersion. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    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 Y1ran/NLP-BERT--ChineseVersion?
    pass
    AI did not name Y1ran/NLP-BERT--ChineseVersion — 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 Y1ran/NLP-BERT--ChineseVersion in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Y1ran/NLP-BERT--ChineseVersion 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 Y1ran/NLP-BERT--ChineseVersion solve, and who is the primary audience?
    pass
    AI did not name Y1ran/NLP-BERT--ChineseVersion — 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?

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Y1ran/NLP-BERT--ChineseVersion — 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