{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/magnitude-a-fast-efficient-universal-vector","title":"Magnitude: A Fast, Efficient Universal Vector Embedding Utility Package","arxiv_id":"1810.11190","date":"2018-10-26","proceeding":"EMNLP 2018 11","authors":["Ajay Patel","Alexander Sands","Chris Callison-Burch","Marianna Apidianaki"],"abstract":"Vector space embedding models like word2vec, GloVe, fastText, and ELMo are\nextremely popular representations in natural language processing (NLP)\napplications. We present Magnitude, a fast, lightweight tool for utilizing and\nprocessing embeddings. Magnitude is an open source Python package with a\ncompact vector storage file format that allows for efficient manipulation of\nhuge numbers of embeddings. Magnitude performs common operations up to 60 to\n6,000 times faster than Gensim. Magnitude introduces several novel features for\nimproved robustness like out-of-vocabulary lookups.","url_abs":"http://arxiv.org/abs/1810.11190v1","url_pdf":"http://arxiv.org/pdf/1810.11190v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"magnitude-a-fast-efficient-universal-vector","repo_url":"https://github.com/plasticityai/magnitude","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[{"method_slug":"bilstm","method_name":"BiLSTM"},{"method_slug":"elmo","method_name":"ELMo"},{"method_slug":"glove","method_name":"GloVe"},{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"},{"method_slug":"fasttext","method_name":"fastText"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1810.11190","atlas_url":"https://app.syntology.ai/?focus=1810.11190","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}