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A feature-packed Python package and vector storage file format for utilizing vector embeddings in machine learning models in a fast, efficient, and simple manner developed by Plasticity. It is primarily intended to be a simpler / faster alternative to Gensim but can be used as a generic key-vector store for domains outside NLP. It offers unique features like out-of-vocabulary lookups and streaming of large models over HTTP. Published in our paper at EMNLP 2018 and available on arXiv.
Features
- A fast, simple vector embedding utility library
- Documentation available
- Examples available
- Basic Out-of-Vocabulary Keys
- Pre-converted Magnitude Formats of Popular Embeddings Models
- Additional Featurization (Parts of Speech, etc.)
Project Samples
Categories
Machine LearningLicense
MIT LicenseFollow Magnitude
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