RRepoGEO

REPOGEO REPORT · LITE

mindspore-lab/mindnlp

Default branch master · commit 7dd3e355 · scanned 6/12/2026, 5:27:24 PM

GitHub: 919 stars · 270 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 mindspore-lab/mindnlp, 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
    Elevate the core value proposition to the README's immediate opening

    Why:

    CURRENT
    The current README starts with a generic H1 "MindNLP" followed by a tagline. The core "bridges the gap" statement is in a later section.
    COPY-PASTE FIX
    Integrate the "MindNLP bridges the gap between HuggingFace's massive model ecosystem and MindSpore's hardware acceleration" statement directly under the main title/tagline, perhaps as the first sentence of the introductory paragraph.
  • mediumtopics#2
    Add topics for framework compatibility and model interoperability

    Why:

    CURRENT
    deep-learning, diffusion-models, huggingface, large-language-models, llm, mindspore, natural-language-processing, nlp, nlp-library, python, vlm
    COPY-PASTE FIX
    deep-learning, diffusion-models, framework-compatibility, huggingface, large-language-models, llm, mindspore, model-interoperability, natural-language-processing, nlp, nlp-library, python, vlm
  • lowreadme#3
    Add a 'Comparison with Alternatives' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., "## 🆚 Comparison with Alternatives" or "## 💡 How MindNLP Differs", explaining how MindNLP's focus on HuggingFace-MindSpore compatibility differs from general-purpose model optimization tools like ONNX Runtime, OpenVINO, or Apache TVM.

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 mindspore-lab/mindnlp
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ONNX Runtime
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ONNX Runtime · recommended 2×
  2. TensorRT · recommended 2×
  3. Apache TVM · recommended 2×
  4. OpenVINO · recommended 1×
  5. JAX · recommended 1×
  • CATEGORY QUERY
    How can I run popular large language and diffusion models on a different deep learning ecosystem?
    you: not recommended
    AI recommended (in order):
    1. ONNX Runtime
    2. OpenVINO
    3. TensorRT
    4. JAX
    5. Apache TVM
    6. Core ML

    AI recommended 6 alternatives but never named mindspore-lab/mindnlp. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools offer seamless compatibility for popular AI models with an alternative deep learning framework?
    you: not recommended
    AI recommended (in order):
    1. OpenVINO Toolkit
    2. ONNX Runtime
    3. Apache TVM
    4. TensorRT
    5. MNN

    AI recommended 5 alternatives but never named mindspore-lab/mindnlp. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 mindspore-lab/mindnlp?
    pass
    AI named mindspore-lab/mindnlp explicitly

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

  • If a team adopts mindspore-lab/mindnlp in production, what risks or prerequisites should they evaluate first?
    pass
    AI named mindspore-lab/mindnlp 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 mindspore-lab/mindnlp solve, and who is the primary audience?
    pass
    AI named mindspore-lab/mindnlp explicitly

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

Embed your GEO score

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mindspore-lab/mindnlp — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite