https://github.com/danieldk/snakefusion

🐍 A slim, low-maintenance Python wrapper for finalfusion.
https://github.com/danieldk/snakefusion

Last synced: 29 days ago
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🐍 A slim, low-maintenance Python wrapper for finalfusion.

• Host: GitHub
• URL: https://github.com/danieldk/snakefusion
• Owner: danieldk
• License: other
• Created: 2021-11-30T18:47:36.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2021-12-27T10:29:44.000Z (over 3 years ago)
• Last Synced: 2025-04-23T04:17:10.665Z (29 days ago)
• Language: Rust
• Homepage:
• Size: 284 KB
• Stars: 3
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE-APACHE

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README

        
# 🐍 snakefusion

[![Documentation Status](https://readthedocs.org/projects/snakefusion/badge/?version=latest)](https://snakefusion.readthedocs.io/en/latest/?badge=latest)

[![pypi Version](https://img.shields.io/pypi/v/snakefusion.svg?style=flat-square&logo=pypi&logoColor=white)](https://pypi.org/project/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](https://docs.rs/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](https://rustup.rs) and `setuptools-rust`:

~~~shell

$ pip install --upgrade setuptools-rust

~~~

You can then build and install `snakefusion` in your environment:

~~~shell

$ pip install .

~~~

## Documentation

Read the [snakefusion documentation](https://snakefusion.readthedocs.io/) for a

quickstart and API reference.

You use [finalfrontier](https://finalfusion.github.io/finalfrontier) to train

new embeddings, or download some [pretrained

embeddings](https://finalfusion.github.io/pretrained).