REPOGEO REPORT · LITE

patil-suraj/question_generation

Default branch master · commit c30e2976 · scanned 6/21/2026, 9:17:33 PM

GitHub: 1,143 stars · 349 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 patil-suraj/question_generation, 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 README's opening to clarify it's a ready-to-use QG library/tool

Why:

COPY-PASTE FIX

Add the following text immediately after the H1 title in the README: "This repository provides a practical, ready-to-use library and fine-tuned models for neural question generation using 🤗transformers, enabling developers and researchers to easily integrate QG capabilities into their applications."

mediumreadme#2

Add a 'Key Features' section to highlight differentiators

Why:

COPY-PASTE FIX

Add a new section titled 'Key Features' or 'Why Use This Library?' after the initial positioning statement. List specific benefits like 'Supports answer-aware and end-to-end QG', 'Provides pre-trained models for various tasks', and 'Easy integration with the Hugging Face ecosystem'.

lowhomepage#3

Add a homepage URL to the repository settings

Why:

COPY-PASTE FIX

Set the repository homepage URL in the GitHub repository settings to a relevant project page, documentation, or demo link (e.g., a Hugging Face Spaces demo or a dedicated project website).

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 patil-suraj/question_generation

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

OpenAI API

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

OpenAI API · recommended 1×
Hugging Face Transformers Library · recommended 1×
Google Cloud AI Platform · recommended 1×
Amazon Comprehend · recommended 1×
Amazon SageMaker · recommended 1×

CATEGORY QUERY
How to automatically create questions from text content using natural language generation?
you: not recommended
AI recommended (in order):
1. OpenAI API
2. Hugging Face Transformers Library
3. Google Cloud AI Platform
4. Amazon Comprehend
5. Amazon SageMaker
6. SpaCy
7. NLTK
AI recommended 7 alternatives but never named patil-suraj/question_generation. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Looking for a transformer-based solution to generate questions from input paragraphs.
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers Library (huggingface/transformers)
2. T5
3. BART
4. AllenNLP (allenai/allennlp)
5. Fairseq (facebookresearch/fairseq)
6. OpenNMT-py (OpenNMT/OpenNMT-py)
7. SpaCy (explosion/spaCy)
AI recommended 7 alternatives but never named patil-suraj/question_generation. 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 patil-suraj/question_generation?
pass
AI did not name patil-suraj/question_generation — 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 patil-suraj/question_generation in production, what risks or prerequisites should they evaluate first?
pass
AI named patil-suraj/question_generation 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 patil-suraj/question_generation solve, and who is the primary audience?
pass
AI did not name patil-suraj/question_generation — 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 patil-suraj/question_generation. 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)

[![RepoGEO](https://repogeo.com/badge/patil-suraj/question_generation.svg)](https://repogeo.com/en/r/patil-suraj/question_generation)

HTML

<a href="https://repogeo.com/en/r/patil-suraj/question_generation"><img src="https://repogeo.com/badge/patil-suraj/question_generation.svg" alt="RepoGEO" /></a>

Pro

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patil-suraj/question_generation — 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