RRepoGEO

REPOGEO REPORT · LITE

brightmart/albert_zh

Default branch master · commit 52149e82 · scanned 5/18/2026, 1:03:27 PM

GitHub: 3,978 stars · 745 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 brightmart/albert_zh, 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
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with your chosen open-source license (e.g., Apache-2.0, MIT).
  • mediumcomparison#2
    Add a dedicated comparison section to the README

    Why:

    COPY-PASTE FIX
    Add a new section titled 'Comparison with BERT' or similar, explicitly detailing parameter reduction, speed improvements, and accuracy retention for Chinese tasks compared to standard BERT models, using bullet points or a table.

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 brightmart/albert_zh
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
BERT
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. BERT · recommended 1×
  2. RoBERTa · recommended 1×
  3. ELECTRA · recommended 1×
  4. MacBERT · recommended 1×
  5. ERNIE · recommended 1×
  • CATEGORY QUERY
    Looking for efficient pre-trained language models for Chinese NLP applications.
    you: not recommended
    AI recommended (in order):
    1. BERT
    2. RoBERTa
    3. ELECTRA
    4. MacBERT
    5. ERNIE
    6. TinyBERT
    7. DistilBERT
    8. XLNet

    AI recommended 8 alternatives but never named brightmart/albert_zh. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need a lightweight BERT-based model for Chinese text processing with good accuracy.
    you: not recommended
    AI recommended (in order):
    1. BERT-tiny (Chinese)
    2. BERT-mini (Chinese)
    3. BERT-small (Chinese)
    4. MacBERT (Chinese)
    5. ELECTRA-small (Chinese)
    6. RoBERTa-tiny/mini (Chinese)

    AI recommended 6 alternatives but never named brightmart/albert_zh. 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 brightmart/albert_zh?
    pass
    AI named brightmart/albert_zh explicitly

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

  • If a team adopts brightmart/albert_zh in production, what risks or prerequisites should they evaluate first?
    pass
    AI named brightmart/albert_zh 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 brightmart/albert_zh solve, and who is the primary audience?
    pass
    AI named brightmart/albert_zh 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 brightmart/albert_zh. 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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brightmart/albert_zh — 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
brightmart/albert_zh — RepoGEO report