RRepoGEO

REPOGEO REPORT · LITE

Lizhen0628/text_classification

Default branch master · commit 76ae3516 · scanned 6/8/2026, 2:13:01 AM

GitHub: 518 stars · 73 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 Lizhen0628/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
  • hightopics#1
    Add specific topics for text classification and model comparison

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    text-classification, deep-learning, nlp, rnn, lstm, gru, fasttext, textcnn, dpcnn, transformers, model-comparison, multi-label-classification
  • highlicense#2
    Add a LICENSE file to clarify usage rights

    Why:

    CURRENT
    (no LICENSE file detected)
    COPY-PASTE FIX
    (Create a LICENSE file, e.g., MIT or Apache-2.0, and add it to the repository root.)
  • mediumreadme#3
    Reposition the README's opening to emphasize model comparison

    Why:

    CURRENT
    # V2

1. V2 版本与V1版本不兼容.v2版本从配置文件可读性，代码复用解耦等方面进行了优化。
2. 添加了多标签文本分类。
    COPY-PASTE FIX
    这个项目提供了一个统一的框架，用于实现和高效比较多种深度学习文本分类模型，包括RNN、LSTM、GRU、FastText、TextCNN、DPCNN、RNN-ATT、LSTM-ATT，并兼容Hugging Face Transformers作为词嵌入。它支持二分类、多分类和多标签分类任务，并专注于代码复用和配置文件的可读性。

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 Lizhen0628/text_classification
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 2×
  2. Keras · recommended 2×
  3. PyTorch Lightning · recommended 2×
  4. fastText · recommended 1×
  5. scikit-learn · recommended 1×
  • CATEGORY QUERY
    What are good libraries for comparing various deep learning text classification models efficiently?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Keras
    3. PyTorch Lightning
    4. fastText
    5. scikit-learn

    AI recommended 5 alternatives but never named Lizhen0628/text_classification. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a Python framework for multi-label text classification using transformer embeddings and CNN/RNN.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch
    3. TensorFlow
    4. Keras
    5. PyTorch Lightning
    6. fast.ai
    7. Flair

    AI recommended 7 alternatives but never named Lizhen0628/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
    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 Lizhen0628/text_classification?
    pass
    AI named Lizhen0628/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 Lizhen0628/text_classification in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Lizhen0628/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 Lizhen0628/text_classification solve, and who is the primary audience?
    pass
    AI named Lizhen0628/text_classification explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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Lizhen0628/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