Overview

Detecting personally identifiable information (PII) in student essays using named entity recognition. The challenge required identifying and classifying PII tokens across thousands of documents with high precision and recall.

Approach

• Synthetic data generation to augment limited training data with realistic PII patterns
• DeBERTa-based NER models fine-tuned for token-level PII classification
• ONNX optimization for inference speed without sacrificing accuracy
• Ensemble strategy combining multiple model checkpoints

Result

121/2048 🥉

• Public leaderboard: 0.970
• Private leaderboard: 0.956

Links

• GitHub Repository

Overview

Answering difficult science questions using large language models. The competition required selecting correct answers from multiple-choice science questions spanning physics, chemistry, biology, and other domains.

Result

194/2664 🥉

Links

• GitHub Repository