# 1e0ng/simhash

A Python Implementation of Simhash Algorithm

Repository: https://github.com/1e0ng/simhash
Canonical: https://ross.abutalabs.com/products/1e0ng-simhash
Language: Python
License: MIT
License Family: permissive
Last push: 2022-03-24T19:22:32+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 4736, "days_push": 1623, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1039, forks 221 (observed 2026-08-28T04:03:20.015663+00:00)

## What it is
A Python implementation of the Simhash algorithm for near-duplicate detection and similarity estimation of text. It provides a small, focused library for computing Simhash fingerprints and comparing their Hamming distance.

## Use cases
- detect near-duplicate documents in a corpus
- deduplicate web pages or news articles
- compute similarity between text strings
- find duplicate content at scale with hashing
- cluster similar text documents
- identify plagiarism or copied content

## When to choose
- you need fast, lightweight near-duplicate detection in Python
- you want a minimal, dependency-free Simhash implementation
- you are processing large text collections where exact matching is insufficient

## When to avoid
- you need full semantic similarity rather than lexical near-duplicates
- you need actively maintained features or support
- you need embeddings-based similarity for short texts

## Facets
- artifact type: library
- maturity: maintenance
- function: nlp, search-engine, parser
- domain: -
- platform: python
- tags: simhash, locality-sensitive-hashing, near-duplicate-detection, text-similarity, hashing, natural-language-processing, algorithms, search

## Member repositories
- 1e0ng/simhash (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:20.015663+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:03:32.919255+00:00, confidence not recorded.
  - readme: https://github.com/1e0ng/simhash (fetched 2026-08-28T04:03:20.015663+00:00, sha 2807b9827f1b)
  - registry_pypi: https://pypi.org/pypi/simhash/json (fetched 2026-08-29T13:04:37.700448+00:00, sha 6b59c3292c9b)
- Data as of 2026-08-30T08:39:29.467469+00:00.