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En Resume Matching Keywords

Developed by Priyanka-Balivada
A spaCy-based named entity recognition model specifically designed for extracting key information from resumes.
Sequence Labeling Supports Multiple LanguagesOpen Source License:MIT#Resume Keyword Extraction#Education Background Recognition#Work Experience Extraction
Downloads 162
Release Time : 2/19/2024

Model Overview

This model is primarily used for resume matching scenarios, capable of identifying key information such as certifications, education, and skills in resumes to automate the resume screening process.

Model Features

Resume Key Information Extraction
Accurately identifies 10 types of key information in resumes, including certifications, education, and skills.
High Recall Rate
Achieves a recall rate of 79.91% in named entity recognition tasks, ensuring the capture of as much relevant information as possible.
Built on spaCy Framework
Constructed on the spaCy framework, making it easy to integrate into existing NLP workflows.

Model Capabilities

Resume Information Extraction
Named Entity Recognition
Text Classification

Use Cases

Human Resources
Automated Resume Screening
Automatically extracts key information such as skills and education from a large number of resumes to improve recruitment efficiency.
Reduces manual screening time and improves screening accuracy.
Talent Management
Talent Pool Development
Automatically extracts and structures resume information for easy establishment and maintenance of a talent database.
Facilitates subsequent talent search and matching.
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