Metadata-Version: 2.4
Name: auto-keyword-ranker
Version: 0.1.0
Summary: Lightweight keyword & keyphrase extraction and ranking using TF-IDF with optional embedding re-ranking.
Author-email: Reya Oberoi <reyaoberoi2005@gmail.com>
License: MIT
Keywords: nlp,keywords,tfidf,embeddings,text
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.21
Requires-Dist: scikit-learn>=1.0
Provides-Extra: embed
Requires-Dist: sentence-transformers>=2.2; extra == "embed"
Provides-Extra: dev
Requires-Dist: pytest; extra == "dev"
Requires-Dist: build; extra == "dev"
Requires-Dist: twine; extra == "dev"
Dynamic: license-file

# auto-keyword-ranker
[![PyPI version](https://img.shields.io/pypi/v/auto-keyword-ranker)](https://pypi.org/project/auto-keyword-ranker)
[![License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE)


Lightweight Python package to extract and rank the most relevant keywords and keyphrases from text.


**Goal:** One-line call to get ranked keywords for articles, blog posts, or short documents. Core approach uses TF-IDF; optional re-ranking with sentence-transformer embeddings.


## Install


Core (TF-IDF only):


```bash
pip install autokeyword
