Presentation link: https://aaudk-my.sharepoint.com/:v:/g/personal/ev52yu_student_aau_dk/IQBhbanv3sOmR4qpqKzNWJ8LASJjBYmnSK-GsC4yRPtShL8?nav=eyJyZWZlcnJhbEluZm8iOnsicmVmZXJyYWxBcHAiOiJPbmVEcml2ZUZvckJ1c2luZXNzIiwicmVmZXJyYWxBcHBQbGF0Zm9ybSI6IldlYiIsInJlZmVycmFsTW9kZSI6InZpZXciLCJyZWZlcnJhbFZpZXciOiJNeUZpbGVzTGlua0NvcHkifX0&e=spnj1M

# 🌱 Green Patent Classification using PatentSBERTa  
### Silver Supervision + Active Learning + Human-in-the-Loop Fine-Tuning

This repository contains a fine-tuned version of **PatentSBERTa** for binary classification of patent claims into:

- **0 — Non-Green**
- **1 — Green Technology**


We created a balanced dataset of 50,000 claims:

- 25,000 Green (silver)
- 25,000 Non-Green (silver)

Stratified split:

- 70% Train
- 15% Pool (for active learning)
- 15% Evaluation

---

# 🧠 Baseline Model (Frozen Embeddings)

We computed claim embeddings using:


A Logistic Regression classifier was trained on frozen embeddings.

### Silver Evaluation Performance

- Accuracy ≈ 77.5%
- Balanced precision and recall across classes

This confirms that domain-specific embeddings capture sustainability semantics effectively even without fine-tuning.

---

# 🔍 Active Learning Strategy

To improve labeling efficiency, we selected high-risk examples from the unlabeled pool using uncertainty sampling:


Where:
- `p_green` is the model’s predicted probability for class 1.
- Higher `u` indicates higher classification uncertainty.

The top 100 most uncertain examples were selected for expert review.

---

# 🤖 LLM → Human HITL Workflow

## LLM Used

qwen2.5:7b-instruct (via Ollama)

The LLM provided:
- Suggested label (0/1)
- Confidence level (low / medium / high)
- Short rationale quoting the claim

Human reviewers assigned the final gold label.

---

# 🔄 Human Overrides (Required Reporting)

Override defined as:


This measures disagreement between the LLM and human expert.

## Example Overrides

1) **LLM 0 → Human 1**  
Claim: Conversion of biological material into energy.  
Reason: Biomass-based energy conversion qualifies as renewable energy under green technology criteria.

2) **LLM 0 → Human 1**  
Claim: Light concentrating optic for photovoltaic device.  
Reason: The invention directly improves solar energy generation, which is renewable and climate-mitigating.

### Observed Pattern

The LLM tends to under-classify cases where renewable energy impact is implied but not explicitly framed as “climate mitigation.”

This highlights the importance of human-in-the-loop review for borderline sustainability cases.

---

# 🚀 Final Fine-Tuning

We fine-tuned:


Training data:
- Silver training split
- + 100 human-labeled gold examples

Training configuration:

- Optimizer: AdamW
- Learning rate: 2e-5
- Epochs: 1
- Max sequence length: 256
- Batch size: 4

---

# 📈 Evaluation

The fine-tuned model was evaluated on:

- Silver evaluation set
- Gold HITL set

Fine-tuning improves performance especially on previously uncertain claims.

---

# 💻 Usage Example

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_path = "your-model-path"

tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForSequenceClassification.from_pretrained(model_path)

text = "A system for converting biomass into renewable energy."

inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=256)

with torch.no_grad():
    outputs = model(**inputs)

prediction = torch.argmax(outputs.logits, dim=-1).item()

print("Green" if prediction == 1 else "Non-Green")