RRepoGEO

REPOGEO REPORT · LITE

netease-youdao/BCEmbedding

Default branch master · commit 00551d2d · scanned 6/30/2026, 12:37:47 PM

GitHub: 1,881 stars · 131 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 netease-youdao/BCEmbedding, 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
  • hightopics#1
    Add specific topics for RAG, embeddings, and rerankers

    Why:

    COPY-PASTE FIX
    rag, embeddings, reranker, multilingual, crosslingual, nlp, deep-learning, sentence-transformers, large-language-models
  • highreadme#2
    Add a concise introductory paragraph to the README

    Why:

    COPY-PASTE FIX
    BCEmbedding is Netease Youdao's comprehensive open-source toolkit, offering a suite of high-performance bilingual and crosslingual embedding and reranker models specifically designed to enhance Retrieval Augmented Generation (RAG) systems. It provides a unified framework for integrating these models into your RAG workflows, supporting various NLP tasks across multiple languages.
  • mediumhomepage#3
    Add a project homepage URL

    Why:

    COPY-PASTE FIX
    https://github.com/netease-youdao/BCEmbedding

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 netease-youdao/BCEmbedding
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
UKPLab/sentence-transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. UKPLab/sentence-transformers · recommended 2×
  2. E5-Mistral-7B-instruct · recommended 1×
  3. Cohere Embed v3 · recommended 1×
  4. OpenAI `text-embedding-3-large` · recommended 1×
  5. XLM-R · recommended 1×
  • CATEGORY QUERY
    Which embedding models perform well for multilingual retrieval augmented generation applications?
    you: not recommended
    AI recommended (in order):
    1. E5-Mistral-7B-instruct
    2. Cohere Embed v3
    3. OpenAI `text-embedding-3-large`
    4. XLM-R
    5. LaBSE
    6. mBERT
    7. MiniLM-L6-v2

    AI recommended 7 alternatives but never named netease-youdao/BCEmbedding. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking effective embedding and reranker models to improve RAG system accuracy.
    you: not recommended
    AI recommended (in order):
    1. OpenAI Embeddings
    2. Cohere Rerank
    3. Mistral Embeddings
    4. BGE (BAAI General Embedding) models (BAAI-ZLAB/BGE)
    5. E5-large-v2 (microsoft/unilm)
    6. Voyage AI Embeddings
    7. Sentence-BERT (UKPLab/sentence-transformers)
    8. Cross-Encoder (UKPLab/sentence-transformers)
    9. OpenAI Reranker

    AI recommended 9 alternatives but never named netease-youdao/BCEmbedding. 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 netease-youdao/BCEmbedding?
    pass
    AI did not name netease-youdao/BCEmbedding — 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 netease-youdao/BCEmbedding in production, what risks or prerequisites should they evaluate first?
    pass
    AI named netease-youdao/BCEmbedding 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 netease-youdao/BCEmbedding solve, and who is the primary audience?
    pass
    AI named netease-youdao/BCEmbedding explicitly

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

Embed your GEO score

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netease-youdao/BCEmbedding — 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