REPOGEO REPORT · LITE

yym6472/ConSERT

Default branch master · commit 03c9e8ff · scanned 6/16/2026, 10:48:02 AM

GitHub: 542 stars · 79 forks

AI VISIBILITY SCORE

30 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 0 warn · 1 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 yym6472/ConSERT, 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 the repository

Why:

COPY-PASTE FIX

nlp, contrastive-learning, self-supervised-learning, sentence-embeddings, representation-learning, pytorch, transformers

highlicense#2
Add a LICENSE file to the repository
Why:
COPY-PASTE FIX
```
Add a LICENSE file (e.g., MIT or Apache-2.0) to the repository root.
```

mediumreadme#3

Enhance the README's introductory section

Why:

CURRENT

# ConSERT

Code for our ACL 2021 paper - ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer

COPY-PASTE FIX

# ConSERT: A Robust Contrastive Learning Framework for Self-Supervised Sentence Embeddings

Code for our ACL 2021 paper - ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer. This repository provides an effective method for generating high-quality, robust sentence representations, crucial for natural language processing tasks like semantic text similarity.

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 yym6472/ConSERT

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Sentence-BERT (SBERT)

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Sentence-BERT (SBERT) · recommended 1×
SimCSE (Simple Contrastive Learning of Sentence Embeddings) · recommended 1×
E5 (Empathetic Embeddings from Encoder-Decoder Models) · recommended 1×
OpenAI Embeddings · recommended 1×
GTE (General Text Embeddings) · recommended 1×

CATEGORY QUERY
How to generate high-quality self-supervised sentence embeddings for natural language processing tasks?
you: not recommended
AI recommended (in order):
1. Sentence-BERT (SBERT)
2. SimCSE (Simple Contrastive Learning of Sentence Embeddings)
3. E5 (Empathetic Embeddings from Encoder-Decoder Models)
4. OpenAI Embeddings
5. GTE (General Text Embeddings)
6. Instructor-XL
7. CoSENT (Contrastive Learning with Sentence Transformers)
AI recommended 7 alternatives but never named yym6472/ConSERT. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are effective contrastive learning methods for unsupervised semantic text similarity?
you: not recommended
AI recommended (in order):
1. SimCSE
2. ESimCSE
3. DiffCSE
4. CoCLR
5. CT-BERT
6. BERT-flow
AI recommended 6 alternatives but never named yym6472/ConSERT. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
fail
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 yym6472/ConSERT?
pass
AI named yym6472/ConSERT explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts yym6472/ConSERT in production, what risks or prerequisites should they evaluate first?
pass
AI named yym6472/ConSERT 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 yym6472/ConSERT solve, and who is the primary audience?
pass
AI named yym6472/ConSERT explicitly
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 yym6472/ConSERT. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/yym6472/ConSERT.svg)](https://repogeo.com/en/r/yym6472/ConSERT)

HTML

<a href="https://repogeo.com/en/r/yym6472/ConSERT"><img src="https://repogeo.com/badge/yym6472/ConSERT.svg" alt="RepoGEO" /></a>

Pro

Subscribe to Pro for deep diagnoses

yym6472/ConSERT — 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