MultinomialNB ATS Score Predictor
| Property | Value |
|---|---|
| Model Type | Multinomial Naive Bayes |
| Task | Resume-Job Description Matching |
| Accuracy | 89.2% |
| Author | AventIQ-AI |
| Model URL | huggingface.co/AventIQ-AI/multinomialnb-ats-score-predictor |
What is multinomialnb-ats-score-predictor?
The multinomialnb-ats-score-predictor is an advanced machine learning model designed to evaluate how well resumes match job descriptions using Applicant Tracking System (ATS) criteria. Built on the Multinomial Naive Bayes algorithm, it leverages TF-IDF vectorization to analyze and score resume-job description pairs with remarkable accuracy.
Implementation Details
The model employs sophisticated text processing techniques, including TF-IDF vectorization with a maximum of 1000 features. It preprocesses both resumes and job descriptions through careful cleaning steps, removing special characters and standardizing text format. The implementation includes PDF text extraction capabilities and sophisticated scoring mechanisms that calculate the percentage match between job requirements and resume content.
- Text preprocessing with regex-based cleaning
- TF-IDF vectorization for feature extraction
- PDF parsing capabilities for resume processing
- Keyword matching and scoring system
- Visualization tools for score representation
Core Capabilities
- Accurate prediction of ATS scores with 89.2% accuracy
- High precision (85.5%) in identifying well-matched resumes
- Strong recall (84.3%) for relevant resume-job pairs
- Real-time resume evaluation against job descriptions
- Visual representation of matching scores
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its combination of Multinomial Naive Bayes architecture with TF-IDF vectorization, specifically optimized for ATS scoring. Its high accuracy and practical implementation tools make it particularly valuable for real-world recruitment scenarios.
Q: What are the recommended use cases?
The model is ideal for recruitment teams, job seekers, and HR platforms looking to automate the initial screening of resumes against job descriptions. It can be integrated into existing ATS systems or used as a standalone tool for resume optimization.
