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ychafiqui/
en_cv_info_extr

Name: en_cv_info_extr
Availability: InStock

named entity recognition

resume

model-index

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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`	Youssef Chafiqui

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/ychafiqui/en_cv_info_extr/resolve/main/en_cv_info_extr-1.0.0-py3-none-any.whl

Load the model

python

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

Inference using the model

python

doc = nlp('put your resume here')

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