REPOGEO REPORT · LITE

NiuTrans/NLPBook

Default branch main · commit a139dd58 · scanned 6/17/2026, 1:33:00 AM

GitHub: 762 stars · 125 forks

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 NiuTrans/NLPBook, 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

highlicense#1

Add a LICENSE file to the repository

Why:

CURRENT

(no LICENSE file detected — the repo has no recognizable license)

COPY-PASTE FIX

Create a LICENSE file in the root of the repository, specifying the intended license (e.g., CC BY-NC-ND 4.0 for educational content).

highreadme#2

Highlight the bilingual nature of the book in the README

Why:

CURRENT

This is a book on neural networks and large language models in NLP. It is intended for anyone interested in NLP and deep learning. Some of the chapters are drawn from our previously published articles (e.g., Introduction to Transformers: An NLP Perspective and Foundations of Large Language Models), but we have added significant new content.

COPY-PASTE FIX

This is a comprehensive book on neural networks and large language models in NLP, presented in both Chinese and English. It is intended for anyone interested in NLP and deep learning, offering a unique resource for a global audience. Some of the chapters are drawn from our previously published articles (e.g., Introduction to Transformers: An NLP Perspective and Foundations of Large Language Models), but we have added significant new content.

mediumhomepage#3
Add the project homepage to the repository's About section
Why:
COPY-PASTE FIX
```
https://niutrans.github.io/NLPBook
```

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 NiuTrans/NLPBook

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Speech and Language Processing

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Speech and Language Processing · recommended 1×
Deep Learning · recommended 1×
huggingface/transformers · recommended 1×
Neural Networks and Deep Learning · recommended 1×
Natural Language Processing with Transformers · recommended 1×

CATEGORY QUERY
Where can I find a comprehensive guide to neural networks and large language models for NLP?
you: not recommended
AI recommended (in order):
1. Speech and Language Processing
2. Deep Learning
3. Hugging Face Transformers (huggingface/transformers)
4. Neural Networks and Deep Learning
5. Natural Language Processing with Transformers
6. fastai (fastai/fastai)
7. PyTorch (pytorch/pytorch)
AI recommended 7 alternatives but never named NiuTrans/NLPBook. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are the best resources for understanding deep learning foundations in natural language processing?
you: not recommended
AI recommended (in order):
1. Speech and Language Processing" by Jurafsky and Martin
2. Deep Learning" by Goodfellow, Bengio, and Courville
3. Neural Networks and Deep Learning" by Michael Nielsen
4. Natural Language Processing with Deep Learning" (CS224N) Stanford Course
5. Deep Learning for NLP" (Oxford University Course)
6. Transformers for Natural Language Processing" by Denis Rothman
7. Applied Deep Learning for NLP" by Delip Rao and Brian McMahan
AI recommended 7 alternatives but never named NiuTrans/NLPBook. 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 NiuTrans/NLPBook?
pass
AI named NiuTrans/NLPBook explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts NiuTrans/NLPBook in production, what risks or prerequisites should they evaluate first?
pass
AI named NiuTrans/NLPBook 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 NiuTrans/NLPBook solve, and who is the primary audience?
pass
AI named NiuTrans/NLPBook 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 NiuTrans/NLPBook. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/NiuTrans/NLPBook.svg)](https://repogeo.com/en/r/NiuTrans/NLPBook)

HTML

<a href="https://repogeo.com/en/r/NiuTrans/NLPBook"><img src="https://repogeo.com/badge/NiuTrans/NLPBook.svg" alt="RepoGEO" /></a>

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Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite