---
language: en
license: apache-2.0
tags:
- text-classification
- bert
- hiring
- recruitment
- ISOM5240
---

# 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"
)