# stanfordnlp/GloVe

Software in C and data files for the popular GloVe model for distributed word representations, a.k.a. word vectors or embeddings

Repository: https://github.com/stanfordnlp/GloVe
Canonical: https://ross.abutalabs.com/products/glove
Language: C
License: Apache-2.0
License Family: permissive
Last push: 2025-07-27T18:05:10+00:00

## Health v2 (maintenance only)
Score: 38/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 33, release rhythm 8, longevity 100
- inputs: {"age_days": 4019, "days_push": 402, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 7230, forks 1545 (observed 2026-08-28T04:09:57.655784+00:00)

## What it is
Stanford's C implementation of the GloVe model for training distributed word representations (word embeddings), plus downloadable pre-trained vectors on large web corpora. It trains word vectors from co-occurrence statistics and provides 2024-updated pre-trained vector files.

## Use cases
- train word embeddings from a custom corpus
- download pre-trained GloVe word vectors for NLP tasks
- compute word similarity and nearest neighbors
- use word vectors as features in downstream NLP models
- explore word analogy relationships like man -> woman

## When to choose
- you need classic static word embeddings rather than contextual transformer embeddings
- you want to train embeddings on your own corpus with a fast C implementation
- you need lightweight, well-established vectors for semantic similarity or as model features

## When to avoid
- you need contextual embeddings that handle polysemy (use BERT-style models instead)
- you want a Python-native training pipeline
- you need multilingual or sentence-level embeddings

## Facets
- artifact type: library
- maturity: stable
- function: machine-learning, nlp, data-generation
- domain: machine-learning, artificial-intelligence
- platform: windows, cpp, c
- tags: word-embeddings, word-vectors, glove, pretrained-vectors, c-implementation, natural-language-processing, linux, macos

## Member repositories
- stanfordnlp/GloVe (main) score 38

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:57.655784+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 2026-08-29T17:39:47.174986+00:00, confidence not recorded.
  - readme: https://github.com/stanfordnlp/GloVe (fetched 2026-08-28T04:09:57.655784+00:00, sha 3c10684ca99e)
- Data as of 2026-08-30T08:39:29.467469+00:00.