RRepoGEO

REPOGEO REPORT · LITE

yuanzhoulvpi2017/zero_nlp

Default branch main · commit 0404bc27 · scanned 5/15/2026, 12:48:03 AM

GitHub: 3,816 stars · 445 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 yuanzhoulvpi2017/zero_nlp, 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
    Reposition the README H1 to clarify project scope and counter misinterpretation

    Why:

    CURRENT
    # zero to nlp
    COPY-PASTE FIX
    # zero_nlp: 中文NLP端到端训练解决方案 (大模型、数据、模型、训练、推理)
  • mediumtopics#2
    Add more descriptive topics to improve category visibility

    Why:

    CURRENT
    bert, chatglm-6b, clip, gpt, gpt2, huggingface-transformers, llama, llama2, llava, nlp, pytorch, text-generation, transformers
    COPY-PASTE FIX
    bert, chatglm-6b, clip, gpt, gpt2, huggingface-transformers, llama, llama2, llava, nlp, pytorch, text-generation, transformers, nlp-framework, chinese-nlp, llm-training, llm-finetuning, multi-gpu-training, end-to-end-solution, data-processing
  • lowhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/yuanzhoulvpi2017/zero_nlp

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 yuanzhoulvpi2017/zero_nlp
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 2×
  2. PyTorch Lightning · recommended 2×
  3. TensorFlow · recommended 2×
  4. Keras · recommended 2×
  5. DeepSpeed · recommended 2×
  • CATEGORY QUERY
    How to build end-to-end Chinese NLP solutions with large models and extensive data?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Hugging Face Datasets
    3. Hugging Face Hub
    4. accelerate
    5. PaddlePaddle
    6. PaddleNLP
    7. ERNIE
    8. PyTorch Lightning
    9. TensorFlow
    10. Keras
    11. TensorFlow Text
    12. DeepSpeed
    13. Megatron-LM
    14. spaCy
    15. Jieba

    AI recommended 15 alternatives but never named yuanzhoulvpi2017/zero_nlp. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What framework provides comprehensive training and fine-tuning for large NLP models on multiple GPUs?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Accelerate
    3. PyTorch's DistributedDataParallel
    4. PyTorch Lightning
    5. DeepSpeed
    6. Megatron-LM
    7. JAX
    8. Flax
    9. TensorFlow
    10. Keras
    11. tf.distribute.Strategy

    AI recommended 11 alternatives but never named yuanzhoulvpi2017/zero_nlp. 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 yuanzhoulvpi2017/zero_nlp?
    pass
    AI named yuanzhoulvpi2017/zero_nlp explicitly

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

  • If a team adopts yuanzhoulvpi2017/zero_nlp in production, what risks or prerequisites should they evaluate first?
    pass
    AI named yuanzhoulvpi2017/zero_nlp 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 yuanzhoulvpi2017/zero_nlp solve, and who is the primary audience?
    pass
    AI named yuanzhoulvpi2017/zero_nlp 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 yuanzhoulvpi2017/zero_nlp. 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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yuanzhoulvpi2017/zero_nlp — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
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