tmikolov/word2vec

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1592 stars · 546 forks observed · 2026-08-28

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

The original C implementation of word2vec, which trains word embeddings using the Continuous Bag-of-Words (CBOW) and Skip-gram neural network architectures. It includes command-line tools and demo scripts for training word vectors from a text corpus and exploring word similarity.

Use cases

• train word embeddings from a text corpus
• compute distributed representations of words
• find semantically similar words
• run skip-gram or CBOW models
• generate word vectors for NLP pipelines

When to choose

• you need the original, lightweight C implementation of word2vec
• you want to train word embeddings on your own corpus without heavy dependencies
• you need fast CPU-based training with hierarchical softmax or negative sampling

When to avoid

• you need modern contextual embeddings like BERT or transformer models
• you want GPU acceleration or deep learning framework integration
• you need actively maintained features or extensive documentation

Facets

library · maturity maintenance

machine-learning nlp cli machine-learning developer-tools cpp c word2vec word-embeddings cbow skip-gram vector-representations natural-language-processing linux macos

Member repositories

For agents

markdown · JSON · MCP: product_card(name="tmikolov/word2vec")