RRepoGEO

REPOGEO REPORT · LITE

codelion/adaptive-classifier

Default branch main · commit e2e819e2 · scanned 6/3/2026, 3:12:26 PM

GitHub: 556 stars · 39 forks

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 codelion/adaptive-classifier, 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
    Strengthen README's opening sentence for category clarity

    Why:

    CURRENT
    Adaptive Classifier is a PyTorch-based machine learning library that revolutionizes text classification with **continuous learning**, **dynamic class addition**, and **strategic defense against adversarial inputs**.
    COPY-PASTE FIX
    Adaptive Classifier is a specialized PyTorch-based machine learning library designed for **dynamic text classification with continuous learning**, enabling zero-downtime adaptation and strategic defense against adversarial inputs for evolving data streams.
  • mediumtopics#2
    Refine repository topics for sharper AI categorization

    Why:

    CURRENT
    adaptive-learning, adaptive-neural-network, bert, classifier, continous-learning, distilbert, elastic-weight-consolidation, embeddings, faiss, large-language-models, llms, machine-learning, multi-class-classification, multi-label-classification, neural-layers, neural-networks, online-learning, roberta, text-classification, transformers
    COPY-PASTE FIX
    adaptive-learning, adaptive-neural-network, bert, classifier, continous-learning, distilbert, elastic-weight-consolidation, embeddings, faiss, multi-class-classification, multi-label-classification, online-learning, roberta, text-classification, transformers
  • lowcomparison#3
    Add a 'Comparison with Alternatives' section to README

    Why:

    COPY-PASTE FIX
    ## 🆚 Comparison with Alternatives

Adaptive Classifier stands apart from general machine learning frameworks like Hugging Face Transformers or TensorFlow by focusing specifically on continuous, adaptive text classification. Unlike traditional models requiring full retraining, Adaptive Classifier enables dynamic class addition and zero-downtime updates, making it ideal for evolving data streams where concept drift is a concern.

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 codelion/adaptive-classifier
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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 1×
  2. huggingface/datasets · recommended 1×
  3. wandb/wandb · recommended 1×
  4. tiangolo/fastapi · recommended 1×
  5. streamlit/streamlit · recommended 1×
  • CATEGORY QUERY
    How to build a text classification system with continuous learning for evolving data?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. 🤗 Datasets (huggingface/datasets)
    3. Weights & Biases (W&B) (wandb/wandb)
    4. FastAPI (tiangolo/fastapi)
    5. Streamlit (streamlit/streamlit)
    6. Kubeflow (kubeflow/kubeflow)
    7. Seldon Core (SeldonIO/seldon-core)
    8. Apache Kafka (apache/kafka)
    9. TensorFlow Extended (TFX) (tensorflow/tfx)
    10. PyTorch Lightning (Lightning-AI/lightning)
    11. MLflow (mlflow/mlflow)
    12. DVC (Data Version Control) (iterative/dvc)
    13. Scikit-learn (scikit-learn/scikit-learn)
    14. XGBoost (dmlc/xgboost)
    15. Flask (pallets/flask)
    16. Django (django/django)
    17. Amazon SageMaker
    18. AWS Lambda
    19. Amazon Kinesis
    20. Google Cloud Vertex AI
    21. Google Cloud Pub/Sub
    22. Google Cloud Dataflow
    23. Azure Machine Learning
    24. Azure Event Hubs
    25. Azure Data Factory

    AI recommended 25 alternatives but never named codelion/adaptive-classifier. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What libraries allow adaptive neural networks for text classification with zero-downtime updates?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Extended (TFX)
    2. TensorFlow Transform
    3. TensorFlow Model Analysis
    4. TensorFlow Serving
    5. PyTorch
    6. TorchServe
    7. Keras
    8. Scikit-learn
    9. Flask
    10. FastAPI
    11. Gunicorn
    12. Uvicorn
    13. Kubernetes
    14. ONNX Runtime
    15. MLflow

    AI recommended 15 alternatives but never named codelion/adaptive-classifier. 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 codelion/adaptive-classifier?
    pass
    AI named codelion/adaptive-classifier explicitly

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

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

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

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codelion/adaptive-classifier — 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