patentsberta-green-hitl

PatentSBERTa Green Classification (HITL Fine-Tuned)

This model is a fine-tuned version of AI-Growth-Lab/PatentSBERTa for binary classification of green patents.

Task

Binary classification:

• 0 = Not green
• 1 = Green (aligned with CPC Y02*)

Training Data

• 50,000 patent claims (balanced: 25k green / 25k non-green)
• Silver labels derived from CPC Y02 codes
• 100 high-uncertainty examples manually reviewed via Human-in-the-Loop (HITL)
• Gold labels override silver labels for those 100 examples

Methodology

1. Baseline model using frozen embeddings + Logistic Regression
2. Uncertainty sampling: u = 1 − 2 · |p − 0.5|
3. Selection of 100 most uncertain claims
4. LLM suggestion + Human review (HITL)
5. Fine-tuning PatentSBERTa for 1 epoch (lr=2e-5, max_length=256)

Performance

Eval (silver split, 5k examples)

• Accuracy: 0.806
• Precision: 0.802
• Recall: 0.820
• F1: 0.811

Gold 100 (HITL se

Video link: https://aaudk-my.sharepoint.com/:v:/g/personal/xm37wh_student_aau_dk/IQDKHMAiX07vSbSmU2pemhLFAbqbEx4kpKUsH4xu9LE55uQ?e=n5Xegc&nav=eyJyZWZlcnJhbEluZm8iOnsicmVmZXJyYWxBcHAiOiJTdHJlYW1XZWJBcHAiLCJyZWZlcnJhbFZpZXciOiJTaGFyZURpYWxvZy1MaW5rIiwicmVmZXJyYWxBcHBQbGF0Zm9ybSI6IldlYiIsInJlZmVycmFsTW9kZSI6InZpZXcifX0%3D