{"id":18459638,"url":"https://github.com/sdsc-innovation/itembed","last_synced_at":"2025-06-17T05:36:52.336Z","repository":{"id":224907876,"uuid":"764546945","full_name":"sdsc-innovation/itembed","owner":"sdsc-innovation","description":"Python library to train shallow embeddings on unordered sequences","archived":false,"fork":false,"pushed_at":"2024-07-23T08:47:16.000Z","size":22187,"stargazers_count":3,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-23T07:12:41.516Z","etag":null,"topics":["embedding-vectors","python","python-library","word2vec"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/sdsc-innovation.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":"CITATION.cff","codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-02-28T09:23:59.000Z","updated_at":"2024-03-06T15:36:12.000Z","dependencies_parsed_at":"2024-11-06T08:38:26.519Z","dependency_job_id":null,"html_url":"https://github.com/sdsc-innovation/itembed","commit_stats":null,"previous_names":["sdsc-innovation/itembed"],"tags_count":1,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sdsc-innovation%2Fitembed","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sdsc-innovation%2Fitembed/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sdsc-innovation%2Fitembed/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sdsc-innovation%2Fitembed/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sdsc-innovation","download_url":"https://codeload.github.com/sdsc-innovation/itembed/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247792045,"owners_count":20996876,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["embedding-vectors","python","python-library","word2vec"],"created_at":"2024-11-06T08:23:55.639Z","updated_at":"2025-04-08T06:31:49.686Z","avatar_url":"https://github.com/sdsc-innovation.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# `itembed` — Item embeddings\n\nThis 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.\nThe contribution of `itembed` is twofold:\n\n 1. Modifying the base algorithm to handle unordered sequences, which has an impact on the definition of context windows;\n 2. Using the two embedding sets introduced in word2vec for supervised learning.\n\nA similar philosophy is described by Wu et al. in StarSpace and by Barkan and Koenigstein in item2vec.\n`itembed` uses Numba to achieve high performances.\n\n\n## Getting started\n\nInstall from [PyPI](https://pypi.org/project/itembed/):\n\n```\npip install itembed\n```\n\nOr install from source, to ensure latest version:\n\n```\npip install git+https://github.com/sdsc-innovation/itembed.git\n```\n\nPlease refer to the [documentation](http://sdsc-innovation.github.io/itembed) for detailed explanations and examples.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsdsc-innovation%2Fitembed","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsdsc-innovation%2Fitembed","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsdsc-innovation%2Fitembed/lists"}