RRepoGEO

REPOGEO REPORT · LITE

datawhalechina/base-llm

Default branch main · commit f091ae7f · scanned 6/12/2026, 9:53:23 AM

GitHub: 826 stars · 92 forks

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 datawhalechina/base-llm, 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
  • highabout#1
    Update 'About' description to be explicit in English

    Why:

    CURRENT
    从 NLP 到 LLM 的算法全栈教程，在线阅读地址：https://datawhalechina.github.io/base-llm/
    COPY-PASTE FIX
    A comprehensive, full-stack curriculum for learning Natural Language Processing (NLP) foundations up to Large Language Models (LLM), designed for developers. Online course available at: https://datawhalechina.github.io/base-llm/
  • highlicense#2
    Add a LICENSE file

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with a permissive open-source license (e.g., MIT or Apache-2.0) to clarify usage rights.
  • mediumreadme#3
    Add a concise English summary at the very top of the README

    Why:

    COPY-PASTE FIX
    Add the following line as the very first visible text in the README (before any HTML or Chinese H1): `A comprehensive, full-stack curriculum for learning Natural Language Processing (NLP) foundations up to Large Language Models (LLM), designed for developers.`

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 datawhalechina/base-llm
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Course
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Course · recommended 1×
  2. Stanford CS224N: Natural Language Processing with Deep Learning · recommended 1×
  3. fast.ai Practical Deep Learning for Coders · recommended 1×
  4. Coursera's DeepLearning.AI NLP Specialization · recommended 1×
  5. Google's Machine Learning Crash Course · recommended 1×
  • CATEGORY QUERY
    Looking for a comprehensive tutorial to learn natural language processing foundations up to large language models.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Course
    2. Stanford CS224N: Natural Language Processing with Deep Learning
    3. fast.ai Practical Deep Learning for Coders
    4. Coursera's DeepLearning.AI NLP Specialization
    5. Google's Machine Learning Crash Course
    6. Speech and Language Processing (Jurafsky and Martin)

    AI recommended 6 alternatives but never named datawhalechina/base-llm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking resources to understand and implement efficient fine-tuning for large language models.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library
    2. Hugging Face PEFT
    3. DeepSpeed
    4. PyTorch FSDP
    5. bitsandbytes
    6. Accelerate
    7. FlashAttention

    AI recommended 7 alternatives but never named datawhalechina/base-llm. 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 datawhalechina/base-llm?
    pass
    AI named datawhalechina/base-llm explicitly

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

  • If a team adopts datawhalechina/base-llm in production, what risks or prerequisites should they evaluate first?
    pass
    AI named datawhalechina/base-llm 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 datawhalechina/base-llm solve, and who is the primary audience?
    pass
    AI did not name datawhalechina/base-llm — 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?

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  • Brand-free category queries5 vs 2 in Lite
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