SERP-Feature-Classifier: SERP Feature Type Prediction

Type: Academic | Domain: SEO, Search
Hugging Face: syeedalireza/serp-feature-classifier

Multi-label classification of SERP feature types (featured snippet, PAA, local pack, etc.) from query and context.

Author

Alireza Aminzadeh

• Hugging Face: syeedalireza
• LinkedIn: alirezaaminzadeh
• Email: alireza.aminzadeh@hotmail.com

Problem

Understanding which SERP features appear for a query helps content and technical SEO strategy (e.g. snippet optimization, local SEO).

Approach

• Input: Query text, optional context (device, locale).
• Output: Multi-label (featured_snippet, paa, local_pack, knowledge_panel, etc.).
• Models: Transformer-based text classifier (e.g. BERT mini) or sentence-transformers + linear head; optional XGBoost on query features.

Tech Stack

Category	Tools
NLP / DL	Hugging Face Transformers, sentence-transformers
ML	scikit-learn, PyTorch
Data	pandas, NumPy

Setup

pip install -r requirements.txt

Usage

python train.py
python inference.py --query "best coffee shops near me"

Project structure

02_serp-feature-classifier/
├── config.py
├── train.py           # Sentence-transformers + MultiOutputClassifier
├── inference.py       # Single query or batch CSV
├── requirements.txt
├── .env.example
├── data/
│   └── serp_labels.csv   # Sample: query + binary labels per SERP feature
└── models/

Data

• Sample data (included): data/serp_labels.csv — columns: query, featured_snippet, paa, local_pack, knowledge_panel, images (0/1).
• Set DATA_PATH in .env if using another file.

License

MIT.