# danielfrg/word2vec

Python interface to Google word2vec

Repository: https://github.com/danielfrg/word2vec
Canonical: https://ross.abutalabs.com/products/word2vec
Language: C
License: Apache-2.0
License Family: permissive
Topics: python, word2vec, doc2vec
Archived: true
Last push: 2023-04-06T22:45:49+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 4756, "days_push": 1245, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2613, forks 618 (observed 2026-08-28T04:07:04.503457+00:00)

## What it is
A Python wrapper around Google's original word2vec C code, providing training and similarity/analogy tools via Python APIs and command-line scripts. The project is explicitly unmaintained and broken with newer numpy versions.

## Use cases
- train word2vec embeddings from a text corpus
- compute word similarity and distance between vectors
- run word analogy tasks
- generate phrase vectors with word2phrase
- evaluate embedding accuracy on standard benchmarks

## When to choose
- you need the exact original Google word2vec C implementation
- you are on an older environment with legacy numpy versions
- you want simple CLI tools for embedding training and evaluation

## When to avoid
- starting a new project - use gensim, TensorFlow, or PyTorch instead
- you need compatibility with modern numpy versions
- you need maintained code with active issue support

## Facets
- artifact type: library
- maturity: abandoned
- function: machine-learning, nlp, cli
- domain: machine-learning
- platform: python, cli
- tags: word2vec, word-embeddings, doc2vec, google-word2vec, not-maintained, natural-language-processing, linux, macos

## Member repositories
- danielfrg/word2vec (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:04.503457+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 2026-08-30T02:20:38.370673+00:00, confidence not recorded.
  - readme: https://github.com/danielfrg/word2vec (fetched 2026-08-28T04:07:04.503457+00:00, sha 0abfb67f1011)
- Data as of 2026-08-30T08:39:29.467469+00:00.