RRepoGEO

REPOGEO REPORT · LITE

OpenDCAI/DataFlex

Default branch main · commit feccc56a · scanned 6/8/2026, 3:32:08 AM

GitHub: 912 stars · 112 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 OpenDCAI/DataFlex, 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 and clarify the core purpose in the README's opening

    Why:

    COPY-PASTE FIX
    Insert this paragraph immediately after the `# DataFlex` title: 'DataFlex is an advanced data-centric training framework built on top of LLaMA-Factory, designed to enhance Large Language Model (LLM) performance by dynamically selecting, mixing, and reweighting training data samples directly within the training loop.'
  • mediumreadme#2
    Add a 'Why DataFlex?' section to highlight unique value

    Why:

    COPY-PASTE FIX
    Add a new section, for example, after the 'Overview', titled 'Why DataFlex?' with content like: 'Unlike general data quality or active learning tools, DataFlex is specifically engineered as a dynamic training framework for Large Language Models (LLMs). It builds on LLaMA-Factory to provide a unified, reproducible system for data selection, mixing, and reweighting, directly addressing the challenges of optimizing LLM training data.'
  • mediumtopics#3
    Add specific LLM-related topics

    Why:

    CURRENT
    data, data-mixture, data-model-interaction, data-reweighting, data-science, data-selection, model-training
    COPY-PASTE FIX
    data, data-mixture, data-model-interaction, data-reweighting, data-science, data-selection, model-training, large-language-models, llm-training

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 OpenDCAI/DataFlex
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Lightly
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Lightly · recommended 2×
  2. modAL · recommended 2×
  3. ALiPy · recommended 2×
  4. Snorkel · recommended 1×
  5. Cleanlab · recommended 1×
  • CATEGORY QUERY
    How to improve large language model performance by dynamically selecting training data?
    you: not recommended
    AI recommended (in order):
    1. Snorkel
    2. Cleanlab
    3. Argilla
    4. Lightly
    5. Humanloop
    6. Galileo
    7. modAL
    8. ALiPy

    AI recommended 8 alternatives but never named OpenDCAI/DataFlex. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Frameworks for optimizing training data samples to enhance deep learning model accuracy?
    you: not recommended
    AI recommended (in order):
    1. modAL
    2. Lightly
    3. ALiPy
    4. Open Active Learning (OpenAL)
    5. Augmentor
    6. Albumentations
    7. Keras/TensorFlow Data Augmentation Layers

    AI recommended 7 alternatives but never named OpenDCAI/DataFlex. 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 OpenDCAI/DataFlex?
    pass
    AI named OpenDCAI/DataFlex explicitly

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

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

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

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OpenDCAI/DataFlex — 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