# Magnitude: A Fast, Efficient Universal Vector Embedding Utility Package

arXiv: https://arxiv.org/abs/1810.11190 · HTML: https://mlsanity.com/paper/1810.11190/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2018.

## Built on by (most cited first, 3 of 3)

- [XCoref: Cross-document Coreference Resolution in the Wild](https://mlsanity.com/paper/2109.05252/) (2021)
- [DICoE@FinSim-3: Financial Hypernym Detection using Augmented Terms and Distance-based Features](https://mlsanity.com/paper/2109.14906/) (2021)
- [Automatic Correction of Syntactic Dependency Annotation Differences](https://mlsanity.com/paper/2201.05891/) (2022)

## Abstract (arXiv)

Vector space embedding models like word2vec, GloVe, fastText, and ELMo are extremely popular representations in natural language processing (NLP) applications. We present Magnitude, a fast, lightweight tool for utilizing and processing embeddings. Magnitude is an open source Python package with a compact vector storage file format that allows for efficient manipulation of huge numbers of embeddings. Magnitude performs common operations up to 60 to 6,000 times faster than Gensim. Magnitude introduces several novel features for improved robustness like out-of-vocabulary lookups.