RRepoGEO

REPOGEO REPORT · LITE

xuyige/BERT4doc-Classification

Default branch master · commit 9da5d119 · scanned 5/17/2026, 7:03:32 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
22 /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
1 / 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 README's opening to clarify its role as a research implementation of 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 code and experimental results from the paper 'How to Fine-Tune BERT for Text Classification?'. It implements and investigates various fine-tuning strategies for BERT on text classification tasks, offering a practical guide and general solution for researchers and practitioners.
  • mediumtopics#2
    Add more specific topics to reflect the repo's focus on fine-tuning strategies

    Why:

    CURRENT
    bert, natural-language-processing, text-classification
    COPY-PASTE FIX
    bert, natural-language-processing, text-classification, fine-tuning, llm-fine-tuning, bert-fine-tuning, research-code, deep-learning-strategies
  • lowhomepage#3
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://github.com/xuyige/BERT4doc-Classification

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
Hugging Face Transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 2×
  2. Hugging Face PEFT Library · recommended 1×
  3. PyTorch Lightning · recommended 1×
  4. TensorFlow Keras · recommended 1×
  5. TensorFlow Hub · recommended 1×
  • CATEGORY QUERY
    How to effectively fine-tune large language models for document categorization tasks?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Hugging Face PEFT Library
    3. PyTorch Lightning
    4. TensorFlow Keras
    5. TensorFlow Hub
    6. Weights & Biases (W&B)
    7. OpenAI API
    8. Google Cloud Vertex AI
    9. AWS SageMaker
    10. Azure Machine Learning
    11. datasets library from Hugging Face
    12. pandas
    13. evaluate library from Hugging Face
    14. scikit-learn

    AI recommended 14 alternatives but never named xuyige/BERT4doc-Classification. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the recommended strategies for optimizing BERT models on various text classification datasets?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. nlaug
    3. Google Translate API
    4. Helsinki-NLP/opus-mt (Helsinki-NLP/opus-mt)
    5. TextAttack
    6. BioBERT
    7. ClinicalBERT
    8. SciBERT
    9. DistilBERT
    10. TinyBERT
    11. ALBERT
    12. RoBERTa
    13. ELECTRA
    14. AdamW
    15. Hugging Face Trainer API
    16. torch.cuda.amp
    17. tf.keras.mixed_precision
    18. EarlyStoppingCallback
    19. Optuna
    20. Ray Tune
    21. ONNX Runtime
    22. TensorRT
    23. ONNX
    24. torch.nn.utils.prune
    25. TensorFlow Model Optimization Toolkit

    AI recommended 25 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 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?

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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