REPOGEO REPORT · LITE

brightmart/text_classification

Default branch master · commit 091ff991 · scanned 6/25/2026, 11:17:44 AM

GitHub: 7,938 stars · 2,536 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)

3 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 brightmart/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 to clarify scope and maintenance status

Why:

CURRENT

Text Classification
The purpose of this repository is to explore text classification methods in NLP with deep learning.

COPY-PASTE FIX

Text Classification
This repository provides a collection of classic deep learning models for text classification, primarily implemented in TensorFlow 1.x. It serves as a valuable resource for understanding baseline architectures. Please note: This project is no longer actively maintained since 2019 and is provided for historical reference and learning purposes.

mediumhomepage#2
Add a homepage URL
Why:
COPY-PASTE FIX
```
https://github.com/brightmart/text_classification
```

lowtopics#3

Add 'tensorflow-1.x' to topics

Why:

CURRENT

attention-mechanism, classification, convolutional-neural-networks, fasttext, memory-networks, multi-class, multi-label, nlp, sentence-classification, tensorflow, text-classification, textcnn, textrnn

COPY-PASTE FIX

attention-mechanism, classification, convolutional-neural-networks, fasttext, memory-networks, multi-class, multi-label, nlp, sentence-classification, tensorflow, tensorflow-1.x, text-classification, textcnn, textrnn

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 brightmart/text_classification

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×
keras-team/keras · recommended 1×
pytorch/pytorch · recommended 1×
facebookresearch/fastText · recommended 1×
explosion/spaCy · recommended 1×

CATEGORY QUERY
How to implement various deep learning models for text classification tasks?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers (huggingface/transformers)
2. Keras (keras-team/keras)
3. PyTorch (pytorch/pytorch)
4. fastText (facebookresearch/fastText)
5. spaCy (explosion/spaCy)
6. Gensim (piskvorky/gensim)
AI recommended 6 alternatives but never named brightmart/text_classification. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are effective deep learning architectures for multi-class and multi-label text classification?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers library
2. Keras
3. PyTorch
4. FastText library
5. fastai library
AI recommended 5 alternatives but never named brightmart/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 brightmart/text_classification?
pass
AI named brightmart/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 brightmart/text_classification in production, what risks or prerequisites should they evaluate first?
pass
AI named brightmart/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 brightmart/text_classification solve, and who is the primary audience?
pass
AI named brightmart/text_classification 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 brightmart/text_classification. 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)

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HTML

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

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brightmart/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