QuickVec is a simple package to make it easy to work with word embeddings. QuickVec supports instantaneous loading of word embeddings after converting them to a native SQLite format. QuickVec is designed to do exactly one thing well: allow you to quickly load word embeddings and look up the vectors for words.
QuickVec requires Python 3.6 and is distributed under the MIT License.
pip install quickvec
QuickVec was created to support NERPy, a named entity recognition framework that uses word embeddings for feature generation. NERPy originally used gensim, but the time and memory required to load a word embedding completely into memory was a large performance bottleneck. NERPy then turned to Magnitude, but its conversion process is quite slow, and its installation process caused problems for NERPy users. The NERPy developers created QuickVec based on the design of Magnitude, but with the goal of creating a package with minimal features and dependencies.
- How does QuickVec compare to gensim's
KeyedVectorsfor loading word embeddings? QuickVec can load word embeddings instantaneously after conversion to its native SQLite-based format, and does not load the whole embedding into memory, making it more memory efficient. However, QuickVec only supports text-format word embeddings files, and in general has far less functionality. - How does QuickVec compare to Magnitude for loading word embeddings? Like Magnitude, QuickVec can instantly load word embeddings after conversion to its native SQLite-based format. QuickVec's conversion process is faster than Magnitude's. However, QuickVec does not support many of Magnitude's features, such as word similarity or generating vectors for out-of-vocabulary words, and QuickVec does not provide pre-converted word embeddings and only supports loading from text-format embeddings.