{"repo":"andrewtavis/kwx","free":true,"listed":false,"github":"https://github.com/andrewtavis/kwx","clone":"git clone https://github.com/andrewtavis/kwx.git","description":"BERT, LDA, and TFIDF based keyword extraction in Python","language":"Python","stars":79,"topics":["keyword-extraction","lda","bert","python","unsupervised-learning","natural-language-processing","nlp","machine-learning","data-science","multilingual"],"license":"BSD-3-Clause","category":"machine-learning","readme_excerpt":"BERT, LDA, and TFIDF based keyword extraction in Python kwx is a toolkit for multilingual keyword extraction based on Google's BERT, Latent Dirichlet Allocation and Term Frequency Inverse Document Frequency. The package provides a suite of methods to process texts of any language to varying degrees and then extract and analyze keywords from the created corpus (see kwx.languages for the various degrees of language support). A unique focus is allowing users to decide which words to not include in outputs, thereby guaranteeing sensible results that are in line with user intuitions. For a thorough overview of the process and techniques see the Google slides, and reference the documentation for explanations of the models and visualization methods. Contents - Installation - Models - BERT - LDA - TFIDF - Word Frequency - Usage - Text Cleaning - Keyword Extraction - Visuals - Topic Number Evaluation - t-SNE - pyLDAvis - Word Cloud - Development environment - To-Do Installation kwx is available for installation via uv (recommended) or pip. For Users For Development Build Back to top. Models Implemented NLP modeling methods within kwx.model include: BERT Bidirectional Encoder Representations from Transformers derives representations of words based on nlp models ran over open-source Wikipedia data. These representations are then leveraged to derive corpus topics. kwx uses sentence-transformers pretrained models. See their GitHub and documentation for the available models. LDA Latent Dir","default_branch":null,"files":null,"tree":[],"storefront":"/r/andrewtavis","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/andrewtavis/kwx/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}