🐍 snakefusion

Introduction

snakefusion is a Python package for reading, writing, and using finalfusion, fastText, floret, GloVe, and word2vec embeddings. This package is a thin wrapper around the Rust finalfusion crate.

snakefusion supports the same types of embeddings as finalfusion:

• Vocabulary:
  • No subwords
  • Subwords
• Embedding matrix:
  • Array
  • Memory-mapped
  • Quantized
• Format:
  • fastText
  • finalfusion
  • floret
  • GloVe
  • word2vec

Building from source

Building snakefusion from source requires a Rust toolchain that is installed through rustup and setuptools-rust:

$ pip install --upgrade setuptools-rust

You can then build and install snakefusion in your environment:

$ pip install .

Usage

Embeddings can be loaded as follows:

import snakefusion

# Loading embeddings in finalfusion format
embeds = snakefusion.Embeddings("myembeddings.fifu")

# Or if you want to memory-map the embedding matrix:
embeds = snakefusion.Embeddings("myembeddings.fifu", mmap=True)

# fastText format
embeds = snakefusion.Embeddings.read_fasttext("myembeddings.bin")

# floret format
embeds = snakefusion.Embeddings.read_floret_text("myembeddings.floret")

# word2vec format
embeds = snakefusion.Embeddings.read_word2vec("myembeddings.w2v")

You can then compute an embedding, perform similarity queries, or analogy queries:

e = embeds.embedding("Tübingen")

# default similarity query for "Tübingen"
embeds.word_similarity("Tübingen")

# similarity query based on a vector, returning the closest embedding to
# the input vector, skipping "Tübingen"
embeds.embeddings_similarity(e, skip={"Tübingen"})

# default analogy query
embeds.analogy("Berlin", "Deutschland", "Amsterdam")

# analogy query allowing "Deutschland" as answer
embeds.analogy("Berlin", "Deutschland", "Amsterdam", mask=(True,False,True))

If you want to operate directly on the full embedding matrix, you can get a copy of this matrix through:

# get copy of embedding matrix, changes to this won't touch the original matrix
e.matrix_copy()

Finally access to the vocabulary is provided through:

v = e.vocab()
# get a list of indices associated with "Tübingen"
v.item_to_indices("Tübingen")

# get a list of `(ngram, index)` tuples for "Tübingen"
v.ngram_indices("Tübingen")

# get a list of subword indices for "Tübingen"
v.subword_indices("Tübingen")

More usage examples can be found in the examples directory.

Where to go from here

• finalfrontier
• finalfusion
• pretrained embeddings