REPOGEO REPORT · LITE

openai/gpt-2-output-dataset

Default branch master · commit b76f67c6 · scanned 6/19/2026, 8:52:53 PM

GitHub: 2,030 stars · 547 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

22 /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

1 / 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 specific topics for AI categorization

Why:

COPY-PASTE FIX

gpt-2, dataset, synthetic-text, ai-detection, machine-generated-text, bias-analysis, nlp-research

highreadme#2

Clarify the README's opening statement for AI detection research

Why:

CURRENT

# gpt-2-output-dataset

This dataset contains:

COPY-PASTE FIX

# gpt-2-output-dataset

A dataset of GPT-2 generated text specifically designed for research into AI detection, bias analysis, and understanding machine-generated content. This dataset contains:

mediumcomparison#3

Add a 'Why use this dataset?' section to the README

Why:

COPY-PASTE FIX

## Why use this dataset?

Unlike generic dataset hubs or collections of human-written text, the gpt-2-output-dataset provides a focused, large-scale corpus of text generated specifically by various GPT-2 models. This makes it uniquely suited for research into detecting machine-generated content, analyzing model biases, and understanding the characteristics of early transformer-based 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

Of all named tools, what % are you?

Top rival

Hugging Face Datasets Hub

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Hugging Face Datasets Hub · recommended 1×
OpenAI's Public Datasets · recommended 1×
Kaggle · recommended 1×
arXiv · recommended 1×
ACL Anthology · recommended 1×

CATEGORY QUERY
Where can I find datasets of synthetic text for training AI detection models?
you: not recommended
AI recommended (in order):
1. Hugging Face Datasets Hub
2. OpenAI's Public Datasets
3. Kaggle
4. arXiv
5. ACL Anthology
6. EMNLP
7. NeurIPS
8. Google Scholar
9. GitHub
10. Google Dataset Search
AI recommended 10 alternatives but never named openai/gpt-2-output-dataset. This is the gap to close.
Show full AI answer
CATEGORY QUERY
I need a large corpus of machine-generated text for bias analysis research.
you: not recommended
AI recommended (in order):
1. Hugging Face Datasets
2. Dolly V2 (databricks/databricks-dolly-15k)
3. OpenAssistant Conversations Dataset (OASST1) (OpenAssistant/oasst1)
4. ShareGPT (anon8231489123/ShareGPT_V3_unfiltered_cleaned_split)
5. Common Crawl
6. GPT-3/GPT-4 API (OpenAI)
7. Anthropic's Claude
8. Google's Gemini
9. EleutherAI's The Pile
10. Reddit
11. Pushshift API
12. Google BigQuery
13. Kaggle Datasets
AI recommended 13 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 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?

Embed your GEO score

Drop this badge into the README of openai/gpt-2-output-dataset. 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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openai/gpt-2-output-dataset — 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