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MODEL_INFO.json (345 Bytes)
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| Name | Modified | Size | Downloads / Week |
|---|---|---|---|
| model.pkl | 2026-08-30 | 1.6 MB | |
| requirements.txt | 2026-08-30 | 21 Bytes | |
| README.md | 2026-08-30 | 1.2 kB | |
| MODEL_INFO.json | 2026-08-30 | 345 Bytes | |
| inference.py | 2026-08-30 | 446 Bytes | |
| metrics.json | 2026-08-30 | 249 Bytes | |
| Totals: 6 Items | 1.6 MB | 3 |
library_name: scikit-learn pipeline_tag: text-classification license: mit tags:
- website-technology-detection
- web-fingerprinting
- web-development
- technology-classification
- synthetic-data datasets:
- website-technology-evidence-dataset
Website Technology Evidence Classifier
A scikit-learn text classifier trained on the companion Website Technology Evidence Dataset.
Method
A FeatureUnion combines word-level TF-IDF (unigrams/bigrams) and character-level TF-IDF (3–5 grams), followed by Logistic Regression.
The character features improve tolerance for URLs, paths, version strings and small formatting differences.
Evaluation
See metrics.json.
Important: the companion dataset is synthetic and fingerprint-driven. Evaluation scores measure performance on this controlled benchmark and must not be presented as accuracy on arbitrary real-world websites.
Output
inference.py returns the top five predicted technologies with confidence scores.
Related project
Website Technology Detector — AzkiWeb
https://azkiweb.com/wordpress-website-agency
https://azkiweb.com/website-design-price
MIT License.
