vincenzoooooo/saskia-sonja-frida-extraversion

Saskia, Sonja & Frida - Personality Detection System: Extraversion Prediction

This model predicts extraversion personality trait levels (low, medium, high) from text input for recruitment applications.

🎯 Model Overview

• —Task: 3-class personality classification
• —Trait: Extraversion (Big Five personality dimension)
• —Classes: Low, Medium, High
• —Domain: Social media → Job interview responses
• —Application: Digital recruitment screening

🏗️ Model Details

• —Base Model: RoBERTa-base
• —Architecture: Transformer encoder + classification head
• —Training Data: PANDORA dataset (Reddit comments)
• —Framework: PyTorch + Transformers
• —Author: Saskia, Sonja & Frida
• —Project: NLP Shared Task 2025 - University of Antwerp

🚀 Quick Start

from transformers import RobertaTokenizer, RobertaForSequenceClassification
import torch
import json
from huggingface_hub import hf_hub_download

# Load model and tokenizer
model = RobertaForSequenceClassification.from_pretrained("vincenzoooooo/saskia-sonja-frida-extraversion")
tokenizer = RobertaTokenizer.from_pretrained("vincenzoooooo/saskia-sonja-frida-extraversion")

# Load label encoder
label_encoder_path = hf_hub_download(repo_id="vincenzoooooo/saskia-sonja-frida-extraversion", filename="label_encoder.json")
with open(label_encoder_path, 'r') as f:
    label_data = json.load(f)
    classes = label_data['classes']  # ['low', 'medium', 'high']

# Make prediction
text = "I love meeting new people and trying new experiences!"
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
outputs = model(**inputs)
predicted_class_id = torch.argmax(outputs.logits, dim=-1).item()
prediction = classes[predicted_class_id]
print(f"Extraversion: {prediction}")

📊 Training Details

• —Optimizer: AdamW (lr=2e-5)
• —Epochs: 2-3
• —Batch Size: 4-8 (memory optimized)
• —Max Sequence Length: 128 tokens
• —Device: CPU/GPU with memory optimization

🎨 Use Cases

• —Digital Recruitment: Screen job candidates
• —HR Analytics: Analyze communication styles
• —Research: Study personality in text
• —Chatbots: Personality-aware responses

⚠️ Limitations

• —Domain Gap: Trained on Reddit, applied to job interviews
• —Bias: May reflect Reddit user demographics
• —Language: English only
• —Context: Short text segments only
• —Small Dataset: Limited training samples

📝 Citation

@misc{saskia_sonja_frida_extraversion_2025,
  title={Saskia, Sonja & Frida - Personality Detection System: Extraversion Prediction},
  author={Saskia, Sonja & Frida},
  year={2025},
  howpublished={\url{https://huggingface.co/vincenzoooooo/saskia-sonja-frida-extraversion}},
  note={NLP Shared Task 2025 - University of Antwerp}
}

🤝 Related Models

Check out our complete personality prediction suite:

• —Openness
• —Conscientiousness
• —Extraversion
• —Agreeableness
• —Emotional Stability

Developed by Saskia, Sonja & Frida for NLP Shared Task 2025 - University of Antwerp