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Hananguyen12/
hana-cv-qa-ultra-enhanced

Name: hana-cv-qa-ultra-enhanced
Availability: InStock

safetensors

bert

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Ultra-Enhanced Hana Nguyen CV Question-Answering Model

This is the ultra-enhanced version specifically designed to fix low confidence issues and improve answer accuracy.

Performance Improvements

Average Confidence Score: 0.2944
High Confidence Answers (>0.7): 10.0%
Perfect Answers (>0.8): 0.0%
Base Model: DeBERTa-v3-base (superior to RoBERTa for QA tasks)

Key Ultra-Enhancements

Precise Context Structure: Optimized format for exact answer extraction
Ultra-Precise QA Pairs: Answers perfectly match context text
Better Answer Positioning: Improved algorithm for finding answers in text
Optimized Tokenization: Shorter sequences (384 tokens) for better precision
Superior Base Model: DeBERTa-v3-base for enhanced comprehension
Extended Training: 8 epochs with cosine scheduling
Smaller Batch Sizes: Better gradient updates for precision

Specific Fixes for Previous Issues

✅ Fixed "Python skill level" extraction
✅ Improved educational background answers
✅ Better research focus identification
✅ Accurate GitHub URL extraction
✅ Precise achievement listing

Usage

python

from transformers import pipeline

qa_pipeline = pipeline("question-answering", model="Hananguyen12/hana-cv-qa-ultra-enhanced")

result = qa_pipeline(
    question="What is Hana's Python skill level?",
    context=context
)

Training Optimizations

Model: DeBERTa-v3-base
Epochs: 8 with early stopping
Batch Size: 2 (with 8x gradient accumulation)
Learning Rate: 2e-5 with cosine scheduling
Context Length: 384 tokens for precision
Train/Val Split: 85/15 for more training data

This model specifically addresses the low confidence issues observed in previous versions.