Usamach/cv_parsing

Information extraction from Resumes/CVs written in English

Model Description

This model is designed for information extraction from resumes/CVs written in English. It employs a transformer-based architecture with spaCy for named entity recognition (NER) tasks. The model aims to parse various sections of resumes, including personal details, education history, professional experience, skills, and certifications, enabling users to extract structured information for further processing or analysis.

Model Details

Feature	Description
Language	English
Task	Named Entity Recognition (NER)
Objective	Information extraction from resumes/CVs
Spacy Components	Transformer, Named Entity Recognition (NER)
Author	Usama Jameel Chaudhary

NER Entities

The model recognizes various entities corresponding to different sections of a resume. Below are the entities used by the model:

Label	Description
'FNAME'	First name
'LNAME'	Last name
'ADDRESS'	Address
'CERTIFICATION'	Certification
'EDUCATION'	Education section
'EMAIL'	Email address
'EXPERIENCE'	Experience section
'HOBBY'	Hobby
'HSKILL'	Hard skill
'LANGUAGE'	Language
'PHONE'	Phone number
'PROFILE'	Profile
'PROJECT'	Project section
'SSKILL'	Soft skill

Evaluation Metrics

Type	Score
F1 score	81.98
Precision	83.33
Recall	80.68

Usage

Presequities

Install spaCy library

pip install spacy

Install Transformers library

pip install transformers

Download the model

pip install https://huggingface.co/Usamach/cv_parsing/resolve/main/en_cv_info_extr-1.0.0-py3-none-any.whl

Load the model

import spacy
nlp = spacy.load("en_cv_info_extr")

Inference using the model

doc = nlp('put your resume here')

for ent in doc.ents:
  print(ent.text, "-", ent.label_)