Cleora is a general-purpose open-source model for efficient, scalable learning of stable and inductive entity embeddings for heterogeneous relational data. It's designed for embedding entities that interact, co-occur, or are present together in a context. Cleora 2.0.0 offers Python native integration, performance optimizations (10x faster embedding times, reduced memory usage), and new features like graph creation from Python iterators, NumPy integration, item attribute support, and adjustable vector projection. Cleora achieves high accuracy and speed by avoiding negative sampling and using efficient matrix multiplication for random walks in a hypergraph. It's been used in various challenges and outperforms other embedding frameworks in terms of speed and accuracy.
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