Word2Vec StaticVectors model

This model is an export of these Word2Vec Vectors for staticvectors. staticvectors enables running inference in Python with NumPy. This helps it maintain solid runtime performance.

This model is a quantized version of the base model. It's using 10x256 Product Quantization.

Usage with StaticVectors

from staticvectors import StaticVectors

model = StaticVectors("neuml/word2vec-quantized")
model.embeddings(["word"])

Given that pre-trained embeddings models can get quite large, there is also a SQLite version that lazily loads vectors.

from staticvectors import StaticVectors

model = StaticVectors("neuml/word2vec-quantized/model.sqlite")
model.embeddings(["word"])