RRepoGEO

REPOGEO REPORT · LITE

valuesimplex/FinBERT

Default branch main · commit cd23a2e4 · scanned 6/6/2026, 9:22:41 PM

GitHub: 915 stars · 139 forks

AI VISIBILITY SCORE
64 /100
Needs work
Category recall
1 / 2
Avg rank #1.0 when recommended
Rule findings
1 pass · 0 warn · 1 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 valuesimplex/FinBERT, 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
    Add a clear English description to the repository's 'About' section

    Why:

    COPY-PASTE FIX
    FinBERT2 is a specialized bidirectional encoder (BERT-style) pre-trained on over 32 billion tokens of high-quality Chinese financial corpus, designed to bridge the deployment gap for LLMs in the financial sector. It excels in Chinese financial text classification and vectorization for RAG systems.
  • mediumhomepage#2
    Add the Hugging Face model page link to the repository homepage field

    Why:

    COPY-PASTE FIX
    https://huggingface.co/valuesimplex-ai-lab/

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 valuesimplex/FinBERT
Avg rank
#1.0
Lower is better. #1 = top recommendation.
Share of voice
8%
Of all named tools, what % are you?
Top rival
FinBERT-Chinese (金融BERT)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. FinBERT-Chinese (金融BERT) · recommended 1×
  2. MacBERT (Masked-Language Model as a Confident Tokenizer for BERT-based Models) - Chinese Financial Domain Version · recommended 1×
  3. ERNIE (Enhanced Representation through kNowledge IntEgration) - Financial Domain (百度文心大模型 金融领域) · recommended 1×
  4. BERT-wwm-ext (Whole Word Masking, extended) - Chinese Financial Domain · recommended 1×
  5. RoBERTa-wwm-ext - Chinese Financial Domain · recommended 1×
  • CATEGORY QUERY
    What are the best pre-trained language models for financial text analysis in Chinese?
    you: not recommended
    AI recommended (in order):
    1. FinBERT-Chinese (金融BERT)
    2. MacBERT (Masked-Language Model as a Confident Tokenizer for BERT-based Models) - Chinese Financial Domain Version
    3. ERNIE (Enhanced Representation through kNowledge IntEgration) - Financial Domain (百度文心大模型 金融领域)
    4. BERT-wwm-ext (Whole Word Masking, extended) - Chinese Financial Domain
    5. RoBERTa-wwm-ext - Chinese Financial Domain
    6. XLNet-Chinese (Financial Domain)

    AI recommended 6 alternatives but never named valuesimplex/FinBERT. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a robust BERT-style model for natural language processing in FinTech applications.
    you: #1
    AI recommended (in order):
    1. FinBERT ← you
    2. BERT-Large, Uncased
    3. RoBERTa-Large
    4. XLNet-Large, Cased
    5. DistilBERT
    6. ALBERT-xxlarge-v2
    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 valuesimplex/FinBERT?
    pass
    AI named valuesimplex/FinBERT explicitly

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

  • If a team adopts valuesimplex/FinBERT in production, what risks or prerequisites should they evaluate first?
    pass
    AI named valuesimplex/FinBERT 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 valuesimplex/FinBERT solve, and who is the primary audience?
    pass
    AI named valuesimplex/FinBERT explicitly

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

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valuesimplex/FinBERT — 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