RRepoGEO

REPOGEO REPORT · LITE

xuyige/BERT4doc-Classification

Default branch master · commit 9da5d119 · scanned 6/28/2026, 11:58:11 PM

GitHub: 641 stars · 101 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
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 xuyige/BERT4doc-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 the README's opening to highlight research on fine-tuning strategies

    Why:

    CURRENT
    This is the code and source for the paper How to Fine-Tune BERT for Text Classification? In this paper, we conduct exhaustive experiments to investigate different fine-tuning methods of BERT on text classification task and provide a general solution for BERT fine-tuning.
    COPY-PASTE FIX
    This repository provides the official code and experimental results from our paper, 'How to Fine-Tune BERT for Text Classification?'. It offers a comprehensive investigation into various BERT fine-tuning strategies for text classification tasks, presenting a general solution and best practices derived from exhaustive experiments. This codebase is ideal for researchers and practitioners exploring optimal fine-tuning approaches for BERT.
  • mediumhomepage#2
    Add the paper's URL as the repository homepage

    Why:

    COPY-PASTE FIX
    The URL of the associated research paper.
  • mediumtopics#3
    Add more specific topics related to fine-tuning strategies and NLP research

    Why:

    CURRENT
    bert, natural-language-processing, text-classification
    COPY-PASTE FIX
    bert, natural-language-processing, text-classification, bert-fine-tuning, nlp-strategies, text-classification-research, transformer-fine-tuning, deep-learning-experiments

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 xuyige/BERT4doc-Classification
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 4 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 4×
  2. huggingface/setfit · recommended 1×
  3. UKPLab/sentence-transformers · recommended 1×
  4. huggingface/peft · recommended 1×
  5. facebookresearch/fastText · recommended 1×
  • CATEGORY QUERY
    How to effectively fine-tune large pre-trained language models for document classification tasks?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library (huggingface/transformers)
    2. Hugging Face Transformers Library (huggingface/transformers)
    3. Hugging Face Transformers Library (huggingface/transformers)
    4. Hugging Face Transformers Library (huggingface/transformers)
    5. SetFit (huggingface/setfit)
    6. Sentence Transformers (UKPLab/sentence-transformers)
    7. PEFT (huggingface/peft)
    8. FastText (facebookresearch/fastText)

    AI recommended 8 alternatives but never named xuyige/BERT4doc-Classification. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best strategies for adapting transformer architectures to specific text categorization problems?
    you: not recommended
    AI recommended (in order):
    1. BERT
    2. RoBERTa
    3. DistilBERT
    4. XLM-RoBERTa
    5. DeBERTa
    6. BioBERT
    7. SciBERT
    8. LegalBERT
    9. GPT-3
    10. GPT-4
    11. T5
    12. TinyBERT
    13. MobileBERT
    14. Hugging Face Transformers library with Adapter-Transformers
    15. EDA (Easy Data Augmentation)
    16. GPT-2

    AI recommended 16 alternatives but never named xuyige/BERT4doc-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 xuyige/BERT4doc-Classification?
    pass
    AI did not name xuyige/BERT4doc-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?

  • If a team adopts xuyige/BERT4doc-Classification in production, what risks or prerequisites should they evaluate first?
    pass
    AI named xuyige/BERT4doc-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 xuyige/BERT4doc-Classification solve, and who is the primary audience?
    pass
    AI named xuyige/BERT4doc-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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xuyige/BERT4doc-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