Embeddings
Embeddings.jl provides functions and data for loading word embeddings such as Word2Vec, FastText, and GloVe.
About Embeddings
Embeddings.jl is a Julia package for loading pretrained word embeddings. It includes Word2Vec (English), GloVe (English), and FastText (hundreds of languages).
It installs with the Julia package manager and downloads the default embedding files the first time they are used. The load_embeddings function returns an EmbeddingTable containing a vocab list and an embeddings matrix, without a built-in word-to-index lookup. File storage is managed through DataDeps.jl.
Key features
- Load pretrained Word2Vec embeddings in Julia
- Load pretrained GloVe embeddings in Julia
- Load FastText embeddings for multiple languages
- Select embedding file by language parameter
- List available files per language and system
- Return vocabulary list with embedding matrix
Best for: Julia users who need pretrained Word2Vec, GloVe, or FastText vectors.
Good for
- Single interface for Word2Vec, GloVe, and FastText embeddings
- Default files download automatically when first needed
- Language parameter selects appropriate default file
Watch out for
- Word2Vec and GloVe described as English; many formats only pretrained in English
- No built-in method to get matrix index from a word
- Pretrained files are downloaded on first use
Embeddings at a glance
- Category
- Development › Developer Tools
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Embeddings FAQ
How do I install the package?
Open the Julia REPL, press ] to enter package mode, then run add Embeddings. No further steps are required.
How do I load embeddings for another language?
Use a language parameter such as FastText_Text{:fr}; if omitted it defaults to English. Use language_files, for example language_files(FastText_Text{:de}), to list files and set default_file_num to choose another.
What does load_embeddings return?
An EmbeddingTable with an embeddings matrix where each column is a word vector, and a vocab vector ordered to match the columns.
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