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Cheykong/HRVibeCheck-Hire-Recommendation-Model

sourceHugging Faceapache-2.0updated 5mo agoView on Hugging Face
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Model Card

HRVibeCheck-Hire-Recommendation-Model

Fine-tuned model for candidate-job matching (Hire / No-Hire)

This model was developed as part of the ISOM5240 Group Project — Deep Learning Business Applications with Python.

Model Details

  • —Base Model: BERT / JobBERT variant
  • —Task: Binary Text Classification (Job Description + Resume)
  • —Input Format: JOB DESCRIPTION: {jd} [SEP] RESUME: {resume}
  • —Output: Probability of Hire (0.0 - 1.0)

Intended Use

  • —Automated resume screening for recruiters
  • —Part of the HRVibeCheck Streamlit application (Pipeline 1)

Training Data

  • —Custom JD-Resume matching dataset
  • —Fine-tuned with Hugging Face Trainer

Performance

Achieved strong validation accuracy during training (exact numbers in project report).

How to Use

python
from transformers import pipeline

pipe = pipeline(
    "text-classification",
    model="Cheykong/HRVibeCheck-Hire-Recommendation-Model"
)