srivihari/resume-job-role-classifier

🔍 Resume Job Role Classifier

A fine-tuned `DistilBERT` model to classify job roles based on resume content. This model is trained to predict the most likely profession from the given resume text, supporting over 14 different job categories.

🧠 Model Details

• —Architecture: DistilBERT (base, uncased)
• —Task: Multi-class text classification
• —Input: Raw resume text (English)
• —Output: Predicted job category label and score

📊 Labels Covered

This model supports classification into the following job categories:

• —Data Science
• —Java Developer
• —Web Designing
• —HR
• —Mechanical Engineer
• —Electrical Engineering
• —Civil Engineer
• —Arts
• —Advocate
• —Sales
• —Health and fitness
• —Business Analyst
• —SAP Developer
• —Automation Testing

🏋️ Training

• —Dataset: Custom dataset containing labeled resumes
• —Split: 80% train / 20% test
• —Metrics:
• —Accuracy: 99–100%
• —F1 Score: ~0.99–1.00 (macro avg)
• —Epochs: 3
• —Batch size: 8
• —Optimizer: AdamW

📥 How to Use

from transformers import pipeline

classifier = pipeline("text-classification", model="srivihari/resume-job-role-classifier")
result = classifier("Experienced data scientist with Python, machine learning, and statistics background.")

print(result)
# Example output:
# [{'label': 'Data Science', 'score': 0.97}]
#Note: If you face any issues like token_type_ids errors, make sure to adjust tokenizer config as below:
#from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

#model = AutoModelForSequenceClassification.from_pretrained("srivihari/resume-job-role-classifier")
#tokenizer = AutoTokenizer.from_pretrained("srivihari/resume-job-role-classifier")
#tokenizer.model_input_names = ["input_ids", "attention_mask"]
#classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)