bazingagin/npc_gzip

Health v2 · maintenance only

Adoption not part of the score

1780 stars · 157 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A Python library and research codebase implementing the parameter-free 'kNN-gzip' text classification method from the ACL 2023 paper, which classifies text using compression-based distances (gzip, lzma, bz2) instead of trained models. It is installable via pip and includes CLI scripts for running experiments on standard datasets or custom data.

Use cases

• classify text without training a model
• low-resource text classification for languages with little data
• run gzip-based kNN classification experiments
• benchmark compression-based classifiers on AG News or DBpedia
• classify a custom labeled dataset with compressors
• reproduce ACL 2023 paper results

When to choose

• you need a parameter-free, training-free text classifier
• you have very few labeled examples per class
• you want to reproduce or extend the kNN-gzip paper
• you want a simple baseline that avoids GPU/model training

When to avoid

• you need state-of-the-art accuracy on large datasets
• you need fast low-latency inference at scale
• you want embeddings or fine-tuning with transformers
• your text is very long, since compression distances scale poorly

Facets

library · maturity maintenance

nlp machine-learning compression cli machine-learning data-science python cli cross-platform text-classification gzip-classifier knn low-resource-nlp research-code acl2023 natural-language-processing

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markdown · JSON · MCP: product_card(name="bazingagin/npc_gzip")