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Python Flask spaCy

CV parser

This project is implementing customized Name Entity Recognition (NER) by spaCy. Recreate 11 classes of name entities and train the model.

tech stack

  • Amazon S3
  • Dataturks
  • Heroku
  • python flask
  • spaCy

customized name entities description

Name: Name of user
Contact: Contact of user, like phone number, email address
Location: Location where user based in or where user want to relocate
Title: Job title user has ever had
Company: Company user has ever worked at
TimePeriod: Period of the time that can be found in CV
Degree: Education
College: Education
Skills: Technical stacks, soft-skills
Years: Preset a year long, like "3+ years"
Others: Links like LinkedIn profile or others

Train your own model

If you want to train your own model, I also wrote a command line tool to train model easily

Installation

python version: 3.7.4

git clone https://github.com/samuelTyh/CVparser.git
cd CVparser
pip install -r requirements.txt

Configuration setting

Set testing data size, iteration times, and early stopping times in config.py

# default hyperparameter in config.py

test_size = 0.3
n_iter = 300
early_stopping = 50

Train

# predict without training
$ python app_cmd.py -f file_you_want_to_parse.pdf -M lib/model_choose_optional/

# train model first then predict
$ python app_cmd.py -f file_you_want_to_parse.pdf

demo site

https://cvparser-demo.herokuapp.com/

Press Choose file and upload buttons to upload CV (only support English version)
Wait for the result from model prediction