BERT-Spam-Job-Posting-Detection-Model

🧠 BERT-Spam-Job-Posting-Detection-Model

A BERT-based binary classifier fine-tuned to detect whether a job posting is fake or real. Ideal for job portals, recruitment platforms, and fraud detection in job advertisements.

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✨ Model Highlights

• 📌 Based on bert-base-uncased
• 🔍 Fine-tuned on a custom dataset of job postings labeled as fake or real
• ⚡ Binary classification: Fake Job Posting vs Real Job Posting
• 💾 Lightweight and optimized for CPU and GPU inference

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🧠 Intended Uses

• Automated detection of fraudulent job postings
• Job board moderation and quality control
• Enhancing recruitment platform security
• Improving user trust in job marketplaces
• Regulatory compliance monitoring for job ads

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🚫 Limitations

• Trained primarily on English-language job postings
• May underperform on postings from less-represented industries or regions
• Not optimized for job descriptions longer than 128 tokens
• Not suitable for multilingual or multimedia job posting content

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🏋️‍♂️ Training Details

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📊 Evaluation Metrics

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🚀 Usage

from transformers import BertTokenizerFast, BertForSequenceClassification
import torch

model_name = "AventIQ-AI/BERT-Spam-Job-Posting-Detection-Model"
tokenizer = BertTokenizerFast.from_pretrained(model_name)
model = BertForSequenceClassification.from_pretrained(model_name)
model.eval()

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

def predict_with_bert(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
    device = next(model.parameters()).device  # Get model device (cpu or cuda)
    inputs = {k: v.to(device) for k, v in inputs.items()}  
    with torch.no_grad():
        logits = model(**inputs).logits

    predicted_class_id = logits.argmax().item()
    return "Fake Job" if predicted_class_id == 1 else "Real Job"

# Example
print(predict_with_bert("Hiring remote data entry clerk for a large online project. Apply now."))
print(predict_with_bert("Looking for a Software Engineer with 5+ years of experience in Python."))

🗂 Repository Structure

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├── model/               # Quantized model files
├── tokenizer_config/    # Tokenizer and vocab files
├── model.safensors/     # Fine-tuned model in safetensors format
├── README.md            # Model card

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🤝 Contributing

Contributions, issues, and feature requests are welcome! Feel free to open a pull request or raise an issue.