ResumeAtlas: Revisiting Resume Classification with Large-Scale Datasets
  and Large Language Models
The increasing reliance on online recruitment platforms coupled with the
adoption of AI technologies has highlighted the critical need for efficient
resume classification methods. However, challenges such as small datasets, lack
of standardized resume templates, and privacy concerns hinder the accuracy and
effectiveness of existing classification models. In this work, we address these
challenges by presenting a comprehensive approach to resume classification. We
curated a large-scale dataset of 13,389 resumes from diverse sources and
employed Large Language Models (LLMs) such as BERT and Gemma1.1 2B for
classification. Our results demonstrate significant improvements over
traditional machine learning approaches, with our best model achieving a top-1
accuracy of 92\% and a top-5 accuracy of 97.5\%. These findings underscore the
importance of dataset quality and advanced model architectures in enhancing the
accuracy and robustness of resume classification systems, thus advancing the
field of online recruitment practices.
667e73ec3b2efcc4414c055b
Ahmed Heakl
claimed_verified
2024-06-28T09:12:59.542Z
656864e12d73834278a8dea7
https://cdn-avatars.huggingface.co/v1/production/uploads/656864e12d73834278a8dea7/sfAWS2eyPtFHb_2GZIypp.jpeg
Ahmed Heakl
user
ahmedheakl
667e73ec3b2efcc4414c055c
Youssef Mohamed
claimed_verified
2024-06-30T20:57:33.427Z
646549e1fafbb0cdb409322e
/avatars/d50714dc6723ac1a1e57e255dfb8a976.svg
Youssef Mohamed Khalil Mohamed
user
youssefkhalil320
667e73ec3b2efcc4414c055d
Noran Mohamed
667e73ec3b2efcc4414c055e
Ali Sharkaway
667e73ec3b2efcc4414c055f
Ahmed Zaky
ResumeAtlas: Revisiting Resume Classification with Large-Scale Datasets
  and Large Language Models
The increasing reliance on online recruitment platforms coupled with the
adoption of AI technologies has highlighted the critical need for efficient
resume classification methods. However, challenges such as small datasets, lack
of standardized resume templates, and privacy concerns hinder the accuracy and
effectiveness of existing classification models. In this work, we address these
challenges by presenting a comprehensive approach to resume classification. We
curated a large-scale dataset of 13,389 resumes from diverse sources and
employed Large Language Models (LLMs) such as BERT and Gemma1.1 2B for
classification. Our results demonstrate significant improvements over
traditional machine learning approaches, with our best model achieving a top-1
accuracy of 92\% and a top-5 accuracy of 97.5\%. These findings underscore the
importance of dataset quality and advanced model architectures in enhancing the
accuracy and robustness of resume classification systems, thus advancing the
field of online recruitment practices.
A large-scale dataset and advanced large language models like BERT and Gemma1.1 2B improve resume classification accuracy and robustness in online recruitment.
Large Language Models BERT Gemma1.1 2B resume classification dataset quality accuracy robustness