REPOGEO REPORT · LITE

NTMC-Community/MatchZoo-py

Default branch master · commit 0e5c04e1 · scanned 6/4/2026, 1:27:11 PM

GitHub: 500 stars · 108 forks

AI VISIBILITY SCORE

35 /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

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 NTMC-Community/MatchZoo-py, 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 H1 and opening sentence to clarify its role as a toolkit

Why:

CURRENT

# MatchZoo-py 
> PyTorch version of MatchZoo. 
> Facilitating the design, comparison and sharing of deep text matching models.

COPY-PASTE FIX

# MatchZoo-py: A PyTorch Toolkit for Deep Text Matching Models
> MatchZoo-py is a comprehensive PyTorch-based toolkit designed to facilitate the design, comparison, and sharing of deep text matching models.

mediumtopics#2

Add more specific topics to improve categorization

Why:

CURRENT

deep-learning, matching, natural-language-processing, neural-network, pytorch, text, text-matching

COPY-PASTE FIX

deep-learning, matching, natural-language-processing, neural-network, pytorch, text, text-matching, information-retrieval, semantic-matching, ranking, toolkit, framework

lowhomepage#3
Add the project's documentation URL as the homepage
Why:
COPY-PASTE FIX
```
https://matchzoo-py.readthedocs.io/en/latest/
```

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 NTMC-Community/MatchZoo-py

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

huggingface/transformers

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

huggingface/transformers · recommended 1×
Lightning-AI/pytorch-lightning · recommended 1×
keras-team/keras · recommended 1×
explosion/spaCy · recommended 1×
RaRe-Technologies/gensim · recommended 1×

CATEGORY QUERY
What framework helps build and evaluate deep learning models for text similarity tasks?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers (huggingface/transformers)
2. PyTorch Lightning (Lightning-AI/pytorch-lightning)
3. Keras (keras-team/keras)
4. spaCy (explosion/spaCy)
5. Gensim (RaRe-Technologies/gensim)
AI recommended 5 alternatives but never named NTMC-Community/MatchZoo-py. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Looking for a PyTorch library to implement and compare different semantic text matching algorithms.
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers
2. Sentence-Transformers
3. torchtext
4. AllenNLP
5. Keras
AI recommended 5 alternatives but never named NTMC-Community/MatchZoo-py. 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 NTMC-Community/MatchZoo-py?
pass
AI named NTMC-Community/MatchZoo-py explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts NTMC-Community/MatchZoo-py in production, what risks or prerequisites should they evaluate first?
pass
AI named NTMC-Community/MatchZoo-py 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 NTMC-Community/MatchZoo-py solve, and who is the primary audience?
pass
AI named NTMC-Community/MatchZoo-py 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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MARKDOWN (README)

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HTML

<a href="https://repogeo.com/en/r/NTMC-Community/MatchZoo-py"><img src="https://repogeo.com/badge/NTMC-Community/MatchZoo-py.svg" alt="RepoGEO" /></a>

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NTMC-Community/MatchZoo-py — 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