@inproceedings{patel-etal-2018-magnitude,
title = "{M}agnitude: A Fast, Efficient Universal Vector Embedding Utility Package",
author = "Patel, Ajay and
Sands, Alexander and
Callison-Burch, Chris and
Apidianaki, Marianna",
editor = "Blanco, Eduardo and
Lu, Wei",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-2021",
doi = "10.18653/v1/D18-2021",
pages = "120--126",
abstract = "Vector space embedding models like word2vec, GloVe, and fastText 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.",
}
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%0 Conference Proceedings
%T Magnitude: A Fast, Efficient Universal Vector Embedding Utility Package
%A Patel, Ajay
%A Sands, Alexander
%A Callison-Burch, Chris
%A Apidianaki, Marianna
%Y Blanco, Eduardo
%Y Lu, Wei
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F patel-etal-2018-magnitude
%X Vector space embedding models like word2vec, GloVe, and fastText 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.
%R 10.18653/v1/D18-2021
%U https://aclanthology.org/D18-2021
%U https://doi.org/10.18653/v1/D18-2021
%P 120-126
Markdown (Informal)
[Magnitude: A Fast, Efficient Universal Vector Embedding Utility Package](https://aclanthology.org/D18-2021) (Patel et al., EMNLP 2018)
ACL