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widget:
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       John Doe
       123 Main Street, Cityville, CA 12345
       [email protected]
       (555) 123-4567
       linkedin.com/in/johndoe
       Professional Summary
       Experienced and results-driven Data Scientist with a strong background in statistical analysis, machine learning, and data visualization. Proven track record of delivering actionable insights and driving data-driven decision-making processes. Adept at leveraging advanced analytics to solve complex business problems.

      Education
       Master of Science in Data Science
       ABC University, Cityville, CA
       May 2021
       
       Bachelor of Science in Computer Science
       XYZ University, Townsville, CA
       Graduation Date: May 2018
       
       Professional Experience
       Data Scientist | Tech Innovators Inc., Cityville, CA | June 2021 - Present
       Lead data analysis projects, extracting valuable insights to inform business strategies.
       Develop and deploy machine learning models to optimize key processes, resulting in a 15% increase in efficiency.
       Collaborate with cross-functional teams to design and implement data-driven solutions.
       Utilize Python, R, and SQL for data extraction, transformation, and analysis.
       Create compelling data visualizations to communicate findings to non-technical stakeholders.

      Data Analyst | Data Solutions Co., Townsville, CA | January 2019 - May
      2021
       Conducted exploratory data analysis to identify trends, patterns, and anomalies.
       Implemented data cleaning and preprocessing techniques to ensure data quality.
       Produced comprehensive reports and dashboards, aiding in executive decision-making.
       Collaborated with business units to define and refine analytical requirements.

      Skills
       Programming Languages: Python, R
       Data Analysis Tools: Pandas, NumPy
       Machine Learning: Scikit-Learn, TensorFlow, Keras
       Database Management: SQL
       Data Visualization: Matplotlib, Seaborn
       Statistical Analysis: Hypothesis testing, Regression analysis
       Communication: Strong written and verbal communication skills

      Certifications
       Certified Data Scientist (CDS)
       Machine Learning Specialist Certification
       
tags:
  - spacy
  - token-classification
  - cv
  - resume parsing
  - resume extraction
  - named entity recognition
  - resume
language:
  - en
model-index:
  - name: en_cv_info_extr
    results:
      - task:
          name: NER
          type: token-classification
        metrics:
          - name: NER Precision
            type: precision
            value: 0.8333333333
          - name: NER Recall
            type: recall
            value: 0.8067729084
          - name: NER F Score
            type: f_score
            value: 0.8198380567
library_name: spacy
pipeline_tag: token-classification

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-any-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_)