# Active Learning for Patent Classification: Green Technology Identification
This repository contains an Active Learning pipeline designed to classify patent claims into "Green" (Climate Change Mitigation) and "Not Green" categories based on the CPC Y02 taxonomy.
## Project Overview
This project implements a Human-in-the-Loop (HITL) workflow to refine a patent classifier. By combining automated silver labeling with expert human validation of high-uncertainty samples, we improve the model's ability to identify complex environmental technologies.
## Part A: Baseline Model Evaluation
The baseline model utilized **PatentSBERTa** for feature extraction and a **Logistic Regression** classifier trained on 2,000 silver-labeled samples.
**Baseline Performance Metrics:**
* **Macro F1-Score:** 0.7488
* **Macro Precision:** 0.7489
* **Macro Recall:** 0.7488
The baseline demonstrated reasonable accuracy but lacked nuance in identifying technologies outside of primary renewables like wind and s…
# Active Learning for Patent Classification: Green Technology Identification
This repository contains an Active Learning pipeline designed to classify patent claims into "Green" (Climate Change Mitigation) and "Not Green" categories based on the CPC Y02 taxonomy.
## Project Overview
This project implements a Human-in-the-Loop (HITL) workflow to refine a patent classifier. By combining automated silver labeling with expert human validation of high-uncertainty samples, we improve the model's ability to identify complex environmental technologies.
## Part A: Baseline Model Evaluation
The baseline model utilized **PatentSBERTa** for feature extraction and a **Logistic Regression** classifier trained on 2,000 silver-labeled samples.
**Baseline Performance Metrics:**
* **Macro F1-Score:** 0.7488
* **Macro Precision:** 0.7489
* **Macro Recall:** 0.7488
The baseline demonstrated reasonable accuracy but lacked nuance in identifying technologies outside of primary renewables like wind and s…
