INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIII, Issue VI, June 2024
www.ijltemas.in Page 13
Resume Screening App using AI
Bulla Rajesh, Mrinal S
Atria Institute of Technology, Bengaluru, India
DOI : https://doi.org/10.51583/IJLTEMAS.2024.130602
Received: 28 May 2024; Accepted: 08 June 2024; Published: 29 June 2024
Abstract—The flood of resumes in today’s talent acquisition landscape makes it difficult for hiring managers and
recruiters to find qualified applicants quickly. This study presents an innovative method to integrate artificial
intelligence techniques into a custom application to expedite the resume screening process. The program increases the
effectiveness of recruitment workflows by a webapp that can be created to take inputs in form of pdf or csv format from
the user and screens the resume. The development of a prototype application is the first step in the research
methodology. It is then thoroughly verified and analyzed.
I. Introduction
The initial phase in the hiring process is resume screening, during which recruiters or hiring managers go through an
extensive collection of resumes in looking for qualified applicants for a position. It entails reviewing each resume to
ascertain whether the candidate has the education, training, and work history needed for the role.
They play a vital role in the hiring process due to increased volume of the received applications. Further subject
expertise is needed for the screening process in order to comprehend the requirements and eligibility for the employment
function.
Resume screening is done using zero shot classification from open-source Hugging Face Bart large mnli model. zero
shot classification enables the app to classify the resumes accurately according to the classes provided.
Principles of Resume Screening App
The resume screening app is primarily dependent upon 3 factors
ï‚·
Labels: For any resume screening process mentioning the labels is important as it defines the goal or the objective
of the classification task
ï‚·
File type: The file type of the resume submitted by the user must be pdf or csv.
ï‚·
Data Cleaning: Before classification process the resume undergoes a data cleaning process to eliminate special
characters, unwanted spaces etc.
Alternative Work Going On In The Field And Their Drawback
Contract or Freelance Work: Although projects involving flexibility and a wide range of skills might be demonstrated,
resume screening applications could find it difficult to assess the value or applicability of such experiences. Applicants
who have chosen alternative career paths may be at a disadvantage as they may give preference to applicants with
traditional job history over those on contract.
Non-Traditional Education and Skills Development: Resume screening tools that give preference to degrees from
traditional educational institutions may provide difficulties for candidates who have followed nontraditional
educational pathways, such as online courses, boot camps, or self-directed study. Similar to this, these applications
might not be able to adequately record or assess abilities learned through unconventional methods.
What our system proposes: User uploads resume and zero shot classification from open-source Hugging Face Bart
large mnli model is applied to the resume. By specifying labels, the zero-shot classification model predicts the suitable
job.
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIII, Issue VI, June 2024
www.ijltemas.in Page 14
II. Data Flow Model
Development of Model
ï‚·
Inputs: resume, labels.
ï‚·
Output: scores of the labels suited for the resume.
Learning Code
This particular problem comes under transfer learning which refers to using a model trained for one task in a different application
than what it was generally trained for.
In zero shot classification we provide the model with a prompt and a sequence of text that describes what we want our model to do,
in natural language.
Loading the model
Facebook/Bart-Large-mnli model is loaded with zero shot classification task from Hugging Face.
zero-shot-classification can improve the effectiveness and productivity of a resume screening software through enabling it to classify
resume applications without requiring explicit training on every category or skill. It is an effective solution for automating and
optimizing the resume screening process because of its scalability and versatility.
III. Data Cleaning
By importing Regular expression library, we can use it for Data Cleaning by replacing special characters, non-ASCII characters,
unwanted spaces, occurrences of RT and cc and unwanted string values with single whitespace character
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIII, Issue VI, June 2024
www.ijltemas.in Page 15
StreamLit WebApp
StreamLit is an open-source python library that facilitates the development and dissemination of stunning, unique online applications
for data science and machine learning. It takes only a few minutes to create and implement robust data applications.
For the resume screening project, the web application can come very useful as users may submit resumes straight into the program
with StreamLit’ s user friendly interface.
Just by submitting the 2 inputs which are labels and resume we can predict the scores of labels suited for the resume.
Scenario 1: When input is a data science resume and input labels provided are machine learning engineer and software developer.
The output scores of labels suited for the job is given below.
We can see that machine learning engineer is a highly suitable job compared to the rest of the labels.
Scenario 2: When input is a software developer resume and input labels are software developer, IoT developer and web developer.
The output scores of labels suited for the job is given below.
We can see that software developer is a highly suitable job compared to the rest of the labels.
IV. Conclusion
Hence, we can see that zero-shot classification can definitely help us in screening of resumes. After integration of this model with
web or app development it can turn into a large-scale application which can ease the burden of the recruiters as they will not have to
manually view a flood of resumes.
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIII, Issue VI, June 2024
www.ijltemas.in Page 16
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