README.md

metadata

title: Actuarial Skills Classifier
emoji: 🎯
colorFrom: purple
colorTo: blue
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: true
license: apache-2.0
tags:
  - actuarial
  - multilabel-classification
  - sentence-classification
  - modernbert
  - skills-extraction
  - career-planning
  - job-analysis
datasets:
  - actuarial-jobs
language:
  - en
metrics:
  - f1
  - precision
  - recall

🎯 Actuarial Skills Classifier

Discover the actuarial and programming skills you need—instantly.

This Hugging Face Space uses multilabel sentence classification trained on 7,000+ real actuarial job postings to instantly map your text to employer-demanded skills. Simply describe a task, role, or career goal, and watch relevant actuarial competencies and coding techniques appear.

🚀 Quick Start

Try these examples:

• "pricing motor portfolio with GLM and Python"
• "life insurance reserving and IFRS 17 implementation"
• "building predictive models for mortality forecasting"

✨ Features

🎯 Skills Analysis

• Instant Mapping: Type any description and get relevant skills in seconds
• Confidence Scores: Each skill shows match percentage (0-100%)
• Smart Categories: Skills organized by type (Programming, Actuarial, Tools, etc.)
• Learning Roadmap: Personalized priority-based development plan

📦 Batch Processing

• Upload CSV or TXT files with multiple descriptions
• Analyze up to 100 job postings simultaneously
• Identify common skills across multiple roles
• Export results for further analysis

🔍 Skills Database

• Browse all 7,000+ learned skills
• Search and filter by keywords
• Understand market demand for specific competencies

📚 Comprehensive Guide

• Detailed usage instructions
• Interpretation guidelines
• Career planning tips
• Example use cases

🎓 Use Cases

Students & Early Career Professionals

Discover what skills you need to learn for your target role:

Input: "I want to become a pricing actuary in motor insurance"
Output: GLM modeling, Python, R, Motor insurance, Pricing techniques, 
        Statistical analysis, Excel, SQL, Risk assessment...

Career Transition

Identify transferable skills and gaps when changing specializations:

Input: "Moving from life insurance reserving to P&C pricing"
Output: [Shows overlap + new skills needed for transition]

Job Analysis

Paste any actuarial job description to extract all required skills:

Input: [Full job posting text]
Output: Complete skill breakdown with confidence scores

Learning Path Planning

Get a structured roadmap for skill development:

Input: "Building machine learning models for insurance pricing"
Output: High Priority → Medium Priority → Nice to Have skills

Hiring & Recruitment

Analyze what skills are commonly required together in the market:

Input: [Multiple job descriptions via batch upload]
Output: Most frequent skill combinations and trends

📊 Model Details

Architecture

• Base Model: answerdotai/ModernBERT-base
• Task: Multi-label sequence classification
• Fine-tuning: Trained on 7,000+ actuarial job postings
• Labels: 100+ unique skills spanning actuarial domains, programming, tools, and soft skills

Training Data

Our model was trained on a diverse dataset of real actuarial job postings covering:

• Industries: Life, P&C, Health, Pensions, Reinsurance
• Regions: Global actuarial markets
• Experience Levels: Entry-level to executive positions
• Time Period: Current market requirements (2023-2025)

Performance Metrics

Metric	Score
F1 Macro	0.XXX
F1 Micro	0.XXX
Precision	0.XXX
Recall	0.XXX

(Replace with actual metrics from your training)

Skills Coverage

The model recognizes skills across multiple categories:

🔤 Programming Languages

• Python, R, SQL, VBA, SAS, C++, Java, JavaScript, Scala

📊 Actuarial Domains

• Pricing, Reserving, Capital Modeling, Valuation
• Life, P&C, Health, Pensions, Reinsurance
• Risk Management, Product Development

🤖 Technical Skills

• Machine Learning, Deep Learning, GLM, GAM
• Statistical Modeling, Data Science, Analytics
• NLP, Computer Vision, Time Series

🛠️ Tools & Software

• Excel, Prophet, ResQ, MoSes, AXIS
• PowerBI, Tableau, Qlik
• Git, Docker, Apache Spark
• TensorFlow, PyTorch, scikit-learn

💼 Business Skills

• Communication, Presentation, Leadership
• Stakeholder Management, Strategy
• Project Management, Team Collaboration

📜 Certifications

• ASA, FSA, ACAS, FCAS
• CERA, CFA, FRM
• Regional qualifications (FIA, DAV, etc.)

🎯 How It Works

1. Input Processing

Your text → Tokenization (ModernBERT tokenizer) → Max 512/1024/2048 tokens

2. Model Inference

Tokenized input → ModernBERT encoder → Multi-label classification head
→ Sigmoid activation → Probability scores for each skill

3. Threshold Application

Raw probabilities → Filter by confidence threshold (default 0.5)
→ Rank by score → Return top K skills

4. Post-Processing

Predicted skills → Categorization → Learning roadmap generation
→ Formatted output for display

💡 Tips for Best Results

✅ DO:

• Be specific about domains: "motor insurance pricing" vs. "insurance"
• Mention tools/languages: "Python GLM model" vs. "statistical model"
• Include context: "IFRS 17 implementation" vs. "accounting"
• Use industry terms: "reserving", "capital modeling", "mortality tables"
• Write 2-3 sentences for richer context

❌ AVOID:

• Very short phrases (under 10 words)
• Generic descriptions: "data analysis job"
• Non-actuarial contexts
• Overly broad terms without specifics

🎚️ Adjusting Parameters

Confidence Threshold:

• 0.3-0.4: Comprehensive (more skills, some less relevant)
• 0.5 (default): Balanced (good mix of precision and recall)
• 0.6-0.7: Precise (fewer skills, highly relevant only)
• 0.8+: Very strict (only very strong matches)

Top K Skills:

• 5-10: Focus on core skills only
• 10-20 (recommended): Comprehensive overview
• 0 (unlimited): See everything above threshold

🔧 Technical Implementation

Requirements

transformers>=4.44.0
torch>=2.0.0
gradio>=4.0.0
pandas
numpy

Local Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "your-username/modernbert-actuarial-skills-classifier"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

text = "pricing motor insurance with GLM and Python"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)

with torch.no_grad():
    outputs = model(**inputs)
    probs = torch.sigmoid(outputs.logits)
    
# Get predictions above threshold
threshold = 0.5
predicted_indices = torch.where(probs[0] > threshold)[0]
predicted_skills = [model.config.id2label[idx.item()] for idx in predicted_indices]

print("Predicted Skills:", predicted_skills)

API Usage

from huggingface_hub import InferenceClient

client = InferenceClient()
result = client.text_classification(
    "pricing motor portfolio with GLM and Python",
    model="your-username/modernbert-actuarial-skills-classifier"
)

print(result)

Batch Processing

texts = [
    "life insurance reserving with IFRS 17",
    "P&C pricing using machine learning",
    "pension scheme valuation"
]

inputs = tokenizer(texts, return_tensors="pt", padding=True, truncation=True)
with torch.no_grad():
    outputs = model(**inputs)
    probs = torch.sigmoid(outputs.logits)

# Process each prediction
for i, text_probs in enumerate(probs):
    print(f"\nText {i+1}: {texts[i]}")
    top_k = torch.topk(text_probs, k=5)
    for score, idx in zip(top_k.values, top_k.indices):
        print(f"  {model.config.id2label[idx.item()]}: {score:.2%}")

📈 Model Training Details

Data Preparation

1. Collection: 7,000+ actuarial job postings from multiple sources
2. Cleaning: Anonymization, deduplication, quality filtering
3. Labeling: Expert-annotated skill tags (100+ unique labels)
4. Splitting: 60% train, 20% validation, 20% test

Training Configuration

- Base Model: answerdotai/ModernBERT-base
- Max Sequence Length: [512/1024/2048] tokens
- Batch Size: [8/16/32] (depends on GPU)
- Learning Rate: 2e-5
- Epochs: 3
- Optimizer: AdamW
- Loss Function: BCEWithLogitsLoss (multi-label)
- Hardware: [GPU type used]

Fine-tuning Process

1. Tokenization: ModernBERT tokenizer with dynamic padding
2. Classification Head: Multi-label binary classifier
3. Training: Cross-entropy loss with label smoothing
4. Evaluation: F1-macro, F1-micro, Hamming loss
5. Optimization: Gradient accumulation, mixed precision (FP16)

🎨 Customization

Adding New Skills

To retrain with additional skills:

1. Prepare labeled data in same format
2. Update label mappings
3. Fine-tune with new data
4. Validate on held-out test set

Adjusting for Other Domains

This architecture can be adapted for other professional domains:

• Software engineering skills
• Medical specializations
• Finance & banking competencies
• Legal practice areas

🤝 Contributing

Ways to improve this tool:

• Report issues or bugs
• Suggest new features
• Share feedback on accuracy
• Contribute training data
• Propose UI/UX improvements

📄 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

🙏 Acknowledgments

• ModernBERT Team: For the excellent base model
• Hugging Face: For hosting infrastructure and tools
• Actuarial Community: For feedback and validation
• Training Data: Sourced from public job boards and postings

📞 Contact & Support

• Model Repository: [Link to your HuggingFace model]
• Issues: Use GitHub Issues or HF Discussions
• Email: [Your contact email]
• LinkedIn: [Your LinkedIn profile]

🔗 Related Resources

For Actuarial Students

• Society of Actuaries
• Casualty Actuarial Society
• Institute and Faculty of Actuaries

For Learning Skills

• Python: Real Python, DataCamp
• R: R for Data Science
• Actuarial Science: Coaching Actuaries
• Machine Learning: Fast.ai, Coursera

Technical Documentation

• Transformers Documentation
• ModernBERT Paper
• Multi-label Classification Guide

📊 Statistics

• Total Skills: 100+ unique labels
• Training Samples: 7,000+ job postings
• Model Size: ~140M parameters
• Inference Speed: <1 second per prediction
• Languages Supported: English (with potential for multilingual expansion)

🚀 Future Enhancements

• Skill clustering and visualization
• Trend analysis over time
• Salary correlation insights
• Course recommendations per skill
• Multi-language support
• Mobile app version
• API endpoint for integration
• Chrome extension for job sites

📝 Citation

If you use this model or tool in your research or application, please cite:

@misc{actuarial-skills-classifier,
  author = {Your Name},
  title = {Actuarial Skills Classifier: Multi-label Classification for Career Planning},
  year = {2025},
  publisher = {HuggingFace},
  journal = {HuggingFace Spaces},
  howpublished = {\url{https://huggingface.co/spaces/your-username/actuarial-skills-classifier}}
}

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