REPOGEO REPORT · LITE

netease-youdao/BCEmbedding

Default branch master · commit 00551d2d · scanned 5/19/2026, 5:13:01 AM

GitHub: 1,880 stars · 130 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 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 to improve categorization

Why:

COPY-PASTE FIX

rag, embedding, reranker, multilingual, cross-lingual, nlp, transformers, chinese-nlp, bilingual

mediumhomepage#2
Set the repository homepage URL
Why:
COPY-PASTE FIX
```
https://twitter.com/YDopensource
```

lowreadme#3

Add a concise opening paragraph to README

Why:

COPY-PASTE FIX

BCEmbedding offers state-of-the-art bilingual and cross-lingual embedding and reranker models, specifically engineered for Retrieval-Augmented Generation (RAG) systems, with a strong focus on Chinese and English language pairs.

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

Of all named tools, what % are you?

Top rival

Cohere Embed v3 (multilingual)

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Cohere Embed v3 (multilingual) · recommended 1×
Cohere Rerank v3 (multilingual) · recommended 1×
OpenAI `text-embedding-3-large` · recommended 1×
gpt-4 · recommended 1×
BAAI/bge-m3 · recommended 1×

CATEGORY QUERY
Seeking robust embedding and reranker models for RAG, especially for multilingual applications.
you: not recommended
AI recommended (in order):
1. Cohere Embed v3 (multilingual)
2. Cohere Rerank v3 (multilingual)
3. OpenAI `text-embedding-3-large`
4. gpt-4
5. BGE-M3 (BAAI/bge-m3)
6. E5-Mistral-7B-instruct (intfloat/multilingual-e5-mistral-7b-instruct)
7. bge-reranker-large (BAAI/bge-reranker-large)
8. Voyage-large-2-instruct
9. Voyage-rerank-v2
10. XLM-R
11. sentence-transformers/paraphrase-multilingual-mpnet-base-v2
12. mBERT
AI recommended 12 alternatives but never named netease-youdao/BCEmbedding. This is the gap to close.
Show full AI answer
CATEGORY QUERY
How to improve RAG performance with better cross-lingual document retrieval and ranking?
you: not recommended
AI recommended (in order):
1. Sentence Transformers
2. paraphrase-multilingual-mpnet-base-v2
3. distiluse-base-multilingual-cased-v2
4. LaBSE
5. Elasticsearch
6. ELSER
7. Weaviate
8. text2vec-contextionary
9. text2vec-transformers
10. Pinecone
11. Qdrant
12. Milvus
13. Hugging Face Transformers
14. bert-base-multilingual-cased
15. xlm-roberta-base
16. mGPT
17. Google Cloud Translation API
18. DeepL API
19. cross-encoder/ms-marco-TinyBERT-L-2-v2
AI recommended 19 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 named netease-youdao/BCEmbedding explicitly
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

Drop this badge into the README of netease-youdao/BCEmbedding. 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)

[![RepoGEO](https://repogeo.com/badge/netease-youdao/BCEmbedding.svg)](https://repogeo.com/en/r/netease-youdao/BCEmbedding)

HTML

<a href="https://repogeo.com/en/r/netease-youdao/BCEmbedding"><img src="https://repogeo.com/badge/netease-youdao/BCEmbedding.svg" alt="RepoGEO" /></a>

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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