REPOGEO REPORT · LITE

plkmo/BERT-Relation-Extraction

Default branch master · commit 65b79447 · scanned 6/10/2026, 3:53:08 AM

GitHub: 602 stars · 134 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

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 plkmo/BERT-Relation-Extraction, 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 opening to highlight specific methodology and purpose

Why:

CURRENT

A PyTorch implementation of the models for the paper "Matching the Blanks: Distributional Similarity for Relation Learning" published in ACL 2019.

COPY-PASTE FIX

This repository offers a PyTorch implementation of the "Matching the Blanks" methodology for robust relation extraction, enabling users to identify semantic relationships between entities using BERT, ALBERT, and BioBERT models, as detailed in the ACL 2019 paper.

mediumhomepage#2
Add homepage URL to repository metadata
Why:
COPY-PASTE FIX
```
https://towardsdatascience.com/bert-s-for-relation-extraction-in-nlp-2c7c3ab487c4
```

lowreadme#3

Add a section clarifying the repo's niche compared to general NLP libraries

Why:

COPY-PASTE FIX

Add a new section, perhaps titled 'Why use this implementation?' or 'Niche & Comparison', with text like: 'While general libraries like Hugging Face Transformers provide broad model access, this repository offers a focused, clear, and effective reference implementation specifically for the "Matching the Blanks" methodology for relation extraction. It is ideal for researchers and practitioners looking to replicate the paper's results or understand this specific approach to semantic relationship identification.'

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 plkmo/BERT-Relation-Extraction

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Hugging Face Transformers Library

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Hugging Face Transformers Library · recommended 1×
BERT · recommended 1×
RoBERTa · recommended 1×
DeBERTa · recommended 1×
SpanBERT · recommended 1×

CATEGORY QUERY
How to extract relationships between entities using transformer models in PyTorch?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers Library
2. BERT
3. RoBERTa
4. DeBERTa
5. SpanBERT
6. PyTorch Geometric
7. Deep Graph Library (DGL)
8. T5
9. GPT-2
10. ERNIE
11. K-BERT
AI recommended 11 alternatives but never named plkmo/BERT-Relation-Extraction. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are effective PyTorch libraries for identifying semantic relationships in text documents?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers
2. AllenNLP
3. PyTorch-Geometric (PyG)
4. SpaCy
5. Stanza
AI recommended 5 alternatives but never named plkmo/BERT-Relation-Extraction. 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 plkmo/BERT-Relation-Extraction?
pass
AI named plkmo/BERT-Relation-Extraction explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts plkmo/BERT-Relation-Extraction in production, what risks or prerequisites should they evaluate first?
pass
AI named plkmo/BERT-Relation-Extraction 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 plkmo/BERT-Relation-Extraction solve, and who is the primary audience?
pass
AI named plkmo/BERT-Relation-Extraction 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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plkmo/BERT-Relation-Extraction — 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