RRepoGEO

REPOGEO REPORT · LITE

openai/gpt-2-output-dataset

Default branch master · commit b76f67c6 · scanned 5/9/2026, 11:27:33 PM

GitHub: 2,029 stars · 550 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 openai/gpt-2-output-dataset, 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
  • hightopics#1
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    gpt-2, ai-generated-text, text-generation, nlp-dataset, ai-detection, bias-detection, synthetic-data, machine-learning-dataset
  • highreadme#2
    Add an explicit purpose statement to the README's introduction

    Why:

    CURRENT
    # gpt-2-output-dataset

This dataset contains:
    COPY-PASTE FIX
    # gpt-2-output-dataset

This dataset provides a comprehensive collection of GPT-2 generated text and WebText for research into AI-generated content detection, model biases, and generation quality analysis.

This dataset contains:
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://openai.com/blog/better-language-models/

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 openai/gpt-2-output-dataset
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Datasets
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Datasets · recommended 1×
  2. Kaggle Datasets · recommended 1×
  3. OpenAI API · recommended 1×
  4. Common Crawl · recommended 1×
  5. GLUE/SuperGLUE Benchmarks · recommended 1×
  • CATEGORY QUERY
    Where can I find large datasets of machine-generated text for AI detection research?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Datasets
    2. Kaggle Datasets
    3. OpenAI API
    4. Common Crawl
    5. GLUE/SuperGLUE Benchmarks

    AI recommended 5 alternatives but never named openai/gpt-2-output-dataset. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need a dataset of synthetic language model outputs to analyze generation quality and biases.
    you: not recommended
    AI recommended (in order):
    1. TruthfulQA
    2. REALTOXICITY
    3. BOLD (Bias in Open-Ended Language Generation)
    4. StereoSet
    5. Crows-Pairs
    6. HellaSwag
    7. XSum (Extreme Summarization)

    AI recommended 7 alternatives but never named openai/gpt-2-output-dataset. 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 openai/gpt-2-output-dataset?
    pass
    AI did not name openai/gpt-2-output-dataset — 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?

  • If a team adopts openai/gpt-2-output-dataset in production, what risks or prerequisites should they evaluate first?
    pass
    AI named openai/gpt-2-output-dataset 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 openai/gpt-2-output-dataset solve, and who is the primary audience?
    pass
    AI named openai/gpt-2-output-dataset explicitly

    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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