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README.md

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+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")