REPOGEO REPORT · LITE

lonePatient/Bert-Multi-Label-Text-Classification

Default branch master · commit 531ee2de · scanned 6/16/2026, 10:48:07 PM

GitHub: 923 stars · 208 forks

AI VISIBILITY SCORE

28 /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

2 / 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 lonePatient/Bert-Multi-Label-Text-Classification, 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 'runnable example' nature

Why:

CURRENT

## Bert multi-label text classification by PyTorch

This repo contains a PyTorch implementation of the pretrained BERT and XLNET model for multi-label text classification.

COPY-PASTE FIX

## Bert multi-label text classification by PyTorch

This repository offers a clean, well-structured, and runnable PyTorch implementation for multi-label text classification using pretrained BERT and XLNET models. It serves as an ideal template for quick setup and experimentation in NLP tasks.

highhomepage#2

Add a homepage URL to the repository

Why:

COPY-PASTE FIX

Add a relevant URL (e.g., a demo, documentation, or project page) to the repository's homepage field.

mediumtopics#3

Refine topics to emphasize 'example' or 'template' aspect and correct typo

Why:

CURRENT

albert, bert, fine-tuning, multi-label-classification, nlp, pytorch, pytorch-implmention, text-classification, transformers, xlnet

COPY-PASTE FIX

albert, bert, fine-tuning, multi-label-classification, nlp, pytorch, pytorch-implementation, text-classification, transformers, xlnet, pytorch-example, nlp-template, multi-label-example

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 lonePatient/Bert-Multi-Label-Text-Classification

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Hugging Face Transformers

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Hugging Face Transformers · recommended 2×
spaCy · recommended 1×
FastText · recommended 1×
scikit-learn · recommended 1×
Keras/TensorFlow · recommended 1×

CATEGORY QUERY
Need a solution for assigning multiple categories to text data using modern NLP techniques.
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers
2. spaCy
3. FastText
4. scikit-learn
5. Keras/TensorFlow
AI recommended 5 alternatives but never named lonePatient/Bert-Multi-Label-Text-Classification. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are good PyTorch libraries for fine-tuning large language models on multi-label tasks?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers
2. PyTorch Lightning
3. Accelerate
4. DeepSpeed
5. Catalyst
AI recommended 5 alternatives but never named lonePatient/Bert-Multi-Label-Text-Classification. 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 lonePatient/Bert-Multi-Label-Text-Classification?
pass
AI named lonePatient/Bert-Multi-Label-Text-Classification explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts lonePatient/Bert-Multi-Label-Text-Classification in production, what risks or prerequisites should they evaluate first?
pass
AI named lonePatient/Bert-Multi-Label-Text-Classification 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 lonePatient/Bert-Multi-Label-Text-Classification solve, and who is the primary audience?
pass
AI did not name lonePatient/Bert-Multi-Label-Text-Classification — 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

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MARKDOWN (README)

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HTML

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Pro

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lonePatient/Bert-Multi-Label-Text-Classification — 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