{"id":24769109,"url":"https://github.com/runtime-error786/text-vectorization","last_synced_at":"2025-10-11T04:07:35.688Z","repository":{"id":270757167,"uuid":"910763737","full_name":"runtime-error786/text-vectorization","owner":"runtime-error786","description":"This repository demonstrates various text vectorization techniques including Bag of Words (BoW), TF-IDF, N-grams, and Word2Vec (CBOW,SKIPGRAM) using nltk,Gensim and Scikit-Learn. The steps outlined here show how to convert textual data into numerical vectors, which are essential for machine learning models.","archived":false,"fork":false,"pushed_at":"2025-01-02T21:47:46.000Z","size":58858,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-23T19:17:26.126Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/runtime-error786.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-01-01T10:52:06.000Z","updated_at":"2025-01-03T11:37:21.000Z","dependencies_parsed_at":"2025-01-02T22:25:00.574Z","dependency_job_id":"485e725a-535b-4d20-a5cf-c828c5360496","html_url":"https://github.com/runtime-error786/text-vectorization","commit_stats":null,"previous_names":["runtime-error786/emotion-detection-text-vectorization","runtime-error786/text-vectorization"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/runtime-error786/text-vectorization","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/runtime-error786%2Ftext-vectorization","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/runtime-error786%2Ftext-vectorization/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/runtime-error786%2Ftext-vectorization/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/runtime-error786%2Ftext-vectorization/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/runtime-error786","download_url":"https://codeload.github.com/runtime-error786/text-vectorization/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/runtime-error786%2Ftext-vectorization/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":279006256,"owners_count":26084060,"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","status":"online","status_checked_at":"2025-10-11T02:00:06.511Z","response_time":55,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":[],"created_at":"2025-01-29T02:47:22.407Z","updated_at":"2025-10-11T04:07:35.663Z","avatar_url":"https://github.com/runtime-error786.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"## Text Vectorization Techniques using nltk,Gensim and Scikit-Learn\nThis repository demonstrates various text vectorization techniques including Bag of Words (BoW), TF-IDF, N-grams, and Word2Vec (CBOW) using nltk,Gensim and Scikit-Learn. The steps outlined here show how to convert textual data into numerical vectors, which are essential for machine learning models.Word2Vec is a popular word embedding technique that uses either Continuous Bag of Words (CBOW) or Skip-gram model to learn vector representations of words based on their context.The CBOW model predicts the target word based on context words, while the Skip-gram model does the reverse by using a word to predict its surrounding context.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fruntime-error786%2Ftext-vectorization","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fruntime-error786%2Ftext-vectorization","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fruntime-error786%2Ftext-vectorization/lists"}