Magnitude: A Fast, Efficient Universal Vector Embedding Utility Package
This provides a more efficient tool for NLP practitioners handling large embedding datasets, though it is incremental as it builds on existing embedding models.
The authors tackled the problem of slow and inefficient manipulation of vector embeddings in NLP by developing Magnitude, a fast, lightweight Python package that performs operations up to 60 to 6,000 times faster than Gensim.
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.