Modelpublic

NeuML/word2vec-quantized

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face

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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

python

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.

python

from staticvectors import StaticVectors

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