This repository generates a company vector embedding given a company name. Many machine learning use cases in business require a numerical representation of company. This repo is designed to aid this process by being easy to understand and generalizable to different companies/industries.
To try out the package, follow the steps below:
- Clone this repository to local machine
- cd in folder root
chmod +x setup.shto make bash file executable./setup.shto run executable - this installs a virtualenv and downloads relevant data- Setup a Bing API on Azure and replace the subscription key in
config.py(there is a free version) - Change the company name in the 'main' function of
quick_start.py, then run
Vector embeddings are often used for natural language processing in machine learning. They are used to represent a concept as a vector - this vector can then be used in a machine learning model. The embedding is created though a combination of an Azure API (to find the company website), scrapy (to do a shallow scrape of the company website) and pre-trained GloVe embeddings.
To return a small number of company embeddings, use quick_start.py. To generate company embeddings at scale, build a
web app using kleinapp.py and Dockerfile and deploy this docker image to the cloud.
Documentation can be found here.
Please do contribute to improve the repository. If you have an issue with the current code/documentation, do open an issue here
This project is licensed under MIT License.