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itembed

Python library to train shallow embeddings on unordered sequences

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

Science Score: 44.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
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    Academic publication links
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (6.5%) to scientific vocabulary

Keywords

embedding-vectors python python-library word2vec
Last synced: 4 months ago · JSON representation ·

Repository

Python library to train shallow embeddings on unordered sequences

Basic Info
  • Host: GitHub
  • Owner: sdsc-innovation
  • License: mit
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 21.2 MB
Statistics
  • Stars: 3
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 1
Topics
embedding-vectors python python-library word2vec
Created almost 2 years ago · Last pushed over 1 year ago
Metadata Files
Readme Changelog License Citation

README.md

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:

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 for detailed explanations and examples.

Owner

  • Name: Swiss Data Science Center - Innovation
  • Login: sdsc-innovation
  • Kind: organization
  • Location: Switzerland

Innovation team at the Swiss Data Science Center.

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Berdat"
  given-names: "Johan"
title: "itembed"
version: 0.5.1
date-released: 2024-02-28
url: "https://github.com/sdsc-innovation/itembed"

GitHub Events

Total
Last Year

Committers

Last synced: 6 months ago

All Time
  • Total Commits: 95
  • Total Committers: 1
  • Avg Commits per committer: 95.0
  • Development Distribution Score (DDS): 0.0
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Johan Berdat j****s@g****m 95

Issues and Pull Requests

Last synced: 4 months ago

All Time
  • Total issues: 0
  • Total pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Total issue authors: 0
  • Total pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
Pull Request Authors
Top Labels
Issue Labels
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Packages

  • Total packages: 1
  • Total downloads:
    • pypi 36 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 4
  • Total maintainers: 1
pypi.org: itembed

word2vec for itemsets

  • Versions: 4
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 36 Last month
Rankings
Dependent packages count: 10.0%
Downloads: 21.4%
Dependent repos count: 21.8%
Average: 22.2%
Stargazers count: 27.8%
Forks count: 29.8%
Maintainers (1)
jojolebarjos
Last synced: 4 months ago

Dependencies

.github/workflows/mkdocs.yml actions
  • actions/checkout v4 composite
  • actions/configure-pages v4 composite
  • actions/deploy-pages v4 composite
  • actions/setup-python v5 composite
  • actions/upload-pages-artifact v3 composite
.github/workflows/pypi.yml actions
  • actions/checkout v4 composite
  • actions/download-artifact v3 composite
  • actions/setup-python v5 composite
  • actions/upload-artifact v3 composite
  • pypa/gh-action-pypi-publish release/v1 composite
  • sigstore/gh-action-sigstore-python v1.2.3 composite
.github/workflows/pytest.yml actions
  • actions/checkout v4 composite
  • actions/setup-python v5 composite
pyproject.toml pypi
  • numba >=0.34, <1.0
  • scipy >=0.16, <2.0
  • tqdm >=1.0
requirements.txt pypi
  • bokeh ==3.3.4
  • ipywidgets ==8.1.2
  • mkdocs ==1.5.3
  • mkdocs-bibtex ==2.12.0
  • mkdocs-gen-files ==0.5.0
  • mkdocs-material ==9.5.11
  • mkdocstrings ==0.24.0
  • mkdocstrings-python ==1.8.0
  • notebook ==7.1.1
  • numba ==0.59.0
  • numpy ==1.26.4
  • pandas ==2.2.1
  • pre-commit ==3.6.2
  • pytest ==8.0.2
  • ruff ==0.2.2
  • scipy ==1.12.0
  • tqdm ==4.66.2
  • umap-learn ==0.5.5