https://github.com/sdsc-innovation/itembed

Python library to train shallow embeddings on unordered sequences
https://github.com/sdsc-innovation/itembed

embedding-vectors python python-library word2vec

Last synced: about 2 months ago
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Python library to train shallow embeddings on unordered sequences

• Host: GitHub
• URL: https://github.com/sdsc-innovation/itembed
• Owner: sdsc-innovation
• License: mit
• Created: 2024-02-28T09:23:59.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-07-23T08:47:16.000Z (5 months ago)
• Last Synced: 2024-10-12T09:10:46.470Z (3 months ago)
• Topics: embedding-vectors, python, python-library, word2vec
• Language: Python
• Homepage:
• Size: 21.2 MB
• Stars: 3
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: LICENSE
  • Citation: CITATION.cff

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README

        
# `itembed` — Item embeddings

This is yet another variation of the well-known word2vec method, proposed by Mikolov et al., applied to unordered sequences, which are commonly referred to as itemsets.

The contribution of `itembed` is twofold:

 1. Modifying the base algorithm to handle unordered sequences, which has an impact on the definition of context windows;

 2. Using the two embedding sets introduced in word2vec for supervised learning.

A similar philosophy is described by Wu et al. in StarSpace and by Barkan and Koenigstein in item2vec.

`itembed` uses Numba to achieve high performances.

## Getting started

Install from [PyPI](https://pypi.org/project/itembed/):

```

pip install itembed

```

Or install from source, to ensure latest version:

```

pip install git+https://github.com/sdsc-innovation/itembed.git

```

Please refer to the [documentation](http://sdsc-innovation.github.io/itembed) for detailed explanations and examples.