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MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n[![CircleCI](https://circleci.com/gh/nagypeterjob/xtractor/tree/master.svg?style=svg)](https://circleci.com/gh/nagypeterjob/xtractor/tree/master)\n\n```\n       _                   _             \n __ __| |_  _ _  __ _  __ | |_  ___  _ _ \n \\ \\ /|  _|| '_|/ _` |/ _||  _|/ _ \\| '_|\n /_\\_\\ \\__||_|  \\__,_|\\__| \\__|\\___/|_|  \n                                         \nxtractor\nTopic extractor with the idea of generating labels using genism.n_similarity\nby Peter Nagy\n```\n## Overview\n\nxtractor is little package which aims to label text automatically harnessing the power of pre-trained word vectors. \u003c/br\u003e\nThe idea is the following: \n- You must provide one or more genism compatible pre-trained word vectors\n- You must define categories with keywords\n- You must provide a tokenized text features you want to label\n- Run the extractor to label input text\n- The extractor digests the cosine distance of each word (vector) in the sentence and each keyword (vector)\n- Then it chooses the most \"similar\" category as label\n\n## Installation\n\n```bash\n$ pip install xtractor\n```\n\n## Usage\n\nSee `example.py` for a more detailed example.\n\n```python\nfrom xtractor import TopicExtractor as te\nextractor = te.TopicExtractor(models=models, categories=categories)\nlabels = extractor.extract(pandas_data_frame)\n```\n\n## Parameters\n\n#### TopicExtractor(models=models, categories=categories)\n\n##### models \n- list of genism compatible models\n\n##### categories\n- list of categories\nFormat:\n\n#### extract(X=pandas_dataframe)\n- input pandas data frame or python list\n- in case X is a pandas dataframe, it must have only one column (the feature column) \n- X can be a regular python `list`\n- the features are expected to be tokenized string (e.g. following format: `['Tokenized', 'string']`)\n- the return value is a regular `list` containing the category names (labels) for each input row respectively (e.g. in case of a 2 row input `['economy', 'sport']`)\n\n## Precision\nIt really depends on the quality of you pre-trained word vector and on the quality of your intuitively defined category keywords.\nIn my use case I have used these [vectors](https://github.com/facebookresearch/fastText/blob/master/pretrained-vectors.md) and played with several iterations of keywords. \u003cbr/\u003e\nI have reached around 69% precision which is not bad. With more carefully picked keywords it can be enhanced.\n\n## F.A.Q.\n\n* Q: Why did you make this?\n  A: Because I looked for a way to automatically label huge amount of (hungarian) text and I found no simple way.\n  \n ## Author\n\n* peter nagy | nagypeterjob@gmail.com\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnagypeterjob%2Fxtractor","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnagypeterjob%2Fxtractor","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnagypeterjob%2Fxtractor/lists"}