RRepoGEO

REPOGEO REPORT · LITE

brightmart/albert_zh

Default branch master · commit 52149e82 · scanned 6/29/2026, 7:10:20 PM

GitHub: 3,981 stars · 742 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
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 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

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

OVERALL DIRECTION
  • highreadme#1
    Reposition README's opening to highlight value for Chinese NLP

    Why:

    CURRENT
    An Implementation of <a href="https://arxiv.org/pdf/1909.11942.pdf">A Lite Bert For Self-Supervised Learning Language Representations</a> with TensorFlow
    COPY-PASTE FIX
    brightmart/albert_zh provides highly efficient, pre-trained ALBERT models specifically optimized for Chinese Natural Language Processing (NLP) tasks. It offers a lightweight alternative to BERT, significantly reducing parameters while retaining strong performance on Chinese benchmarks.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root, clearly stating the chosen open-source license (e.g., Apache-2.0, MIT, etc.) that applies to this project.
  • mediumabout#3
    Refine the repository's 'About' description

    Why:

    CURRENT
    A LITE BERT FOR SELF-SUPERVISED LEARNING OF LANGUAGE REPRESENTATIONS, 海量中文预训练ALBERT模型
    COPY-PASTE FIX
    Lightweight, pre-trained ALBERT models for efficient Chinese NLP. Significantly reduces parameters while maintaining strong performance on Chinese benchmarks.

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
google-research/bert
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. google-research/bert · recommended 1×
  2. pytorch/fairseq · recommended 1×
  3. PaddlePaddle/ERNIE · recommended 1×
  4. hfl/chinese-macbert · recommended 1×
  5. google-research/electra · recommended 1×
  • CATEGORY QUERY
    Looking for efficient pre-trained language models for Chinese text analysis tasks.
    you: not recommended
    AI recommended (in order):
    1. BERT (google-research/bert)
    2. RoBERTa (pytorch/fairseq)
    3. ERNIE (PaddlePaddle/ERNIE)
    4. MacBERT (hfl/chinese-macbert)
    5. ELECTRA (google-research/electra)
    6. XLNet (zihangdai/xlnet)
    7. DistilBERT (huggingface/transformers)

    AI recommended 7 alternatives but never named brightmart/albert_zh. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are lightweight pre-trained models offering good performance on Chinese NLP benchmarks?
    you: not recommended
    AI recommended (in order):
    1. BERT-tiny
    2. MacBERT
    3. RoBERTa-wwm-ext-base
    4. ELECTRA-small
    5. ERNIE-tiny
    6. MiniCPM
    7. DistilBERT-base-chinese

    AI recommended 7 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 did not name brightmart/albert_zh — 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 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?

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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