REPOGEO REPORT · LITE

brightmart/nlp_chinese_corpus

Default branch master · commit 5dc87215 · scanned 5/18/2026, 5:12:01 AM

GitHub: 9,894 stars · 1,556 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

22 /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

1 / 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/nlp_chinese_corpus, 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

Add a direct, descriptive H1 to the README's opening

Why:

CURRENT

The README currently starts with "#### 为中文自然语言处理领域发展贡献语料".

COPY-PASTE FIX

# brightmart/nlp_chinese_corpus: 大规模中文自然语言处理语料 (Large Scale Chinese Corpus for NLP)

This repository provides a massive, curated collection of diverse Chinese text corpora for training and pre-training modern Chinese language models.

mediumreadme#2

Highlight the total scale and diversity of the corpus early in the README

Why:

CURRENT

The scale information is currently listed under "一期目标" and "二期目标" further down the README.

COPY-PASTE FIX

This project aims to provide over 30 million Chinese corpora, including diverse sources like Wikipedia, news articles, high-quality community Q&A (webtext2019zh), and extensive translation data (translation2019zh), continuously expanding to support ultra-large NLP model training.

lowhomepage#3
Add a relevant homepage URL to the repository metadata
Why:
COPY-PASTE FIX
```
https://www.CLUEbenchmarks.com
```

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/nlp_chinese_corpus

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Chinese Wikipedia Dump

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Chinese Wikipedia Dump · recommended 2×
Common Crawl · recommended 2×
Baidu Baike · recommended 2×
Tencent AI Lab Chinese Corpus · recommended 2×
cc_net · recommended 1×

CATEGORY QUERY
Where can I find a large-scale Chinese text corpus for training NLP models?
you: not recommended
AI recommended (in order):
1. Chinese Wikipedia Dump
2. Common Crawl
3. cc_net
4. Baidu Baike
5. Tencent AI Lab Chinese Corpus
6. CLUECorpus2020
7. WMT (Workshop on Machine Translation) Chinese-English Parallel Corpora
8. LDC (Linguistic Data Consortium) Chinese Corpora
9. LDC Chinese Treebank
10. Chinese News Corpora
AI recommended 10 alternatives but never named brightmart/nlp_chinese_corpus. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are good sources for Chinese pre-training data to build custom language models?
you: not recommended
AI recommended (in order):
1. WuDaoCorpora
2. CLUECorpus
3. Baidu Baike
4. Sogou News Corpus
5. Chinese Wikipedia Dump
6. Common Crawl
7. Tencent AI Lab Chinese Corpus
AI recommended 7 alternatives but never named brightmart/nlp_chinese_corpus. 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/nlp_chinese_corpus?
pass
AI did not name brightmart/nlp_chinese_corpus — 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/nlp_chinese_corpus in production, what risks or prerequisites should they evaluate first?
pass
AI named brightmart/nlp_chinese_corpus 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/nlp_chinese_corpus solve, and who is the primary audience?
pass
AI did not name brightmart/nlp_chinese_corpus — 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?

Embed your GEO score

Drop this badge into the README of brightmart/nlp_chinese_corpus. 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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<a href="https://repogeo.com/en/r/brightmart/nlp_chinese_corpus"><img src="https://repogeo.com/badge/brightmart/nlp_chinese_corpus.svg" alt="RepoGEO" /></a>

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brightmart/nlp_chinese_corpus — 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