Upload README.md

README.md

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+---
+tags:
+- patents
+- climate
+- green-technology
+- text-classification
+- patent-classification
+- human-in-the-loop
+- multi-agent
+- patentsberta
+language:
+- en
+pipeline_tag: text-classification
+library_name: transformers
+---
+
+# Green Patent Detection: Multi-Agent HITL + PatentSBERTa
+
+This repository contains an advanced green patent detection workflow built for **binary classification of patent claims** into:
+
+- **1 = Green / climate mitigation related**
+- **0 = Non-green**
+
+The project extends a baseline PatentSBERTa workflow by adding a **Human-in-the-Loop (HITL)** review stage and a **multi-agent debate system** before final fine-tuning.
+
+## Project overview
+
+The goal of this project is to improve green patent detection by combining:
+
+1. **High-risk sample selection** from uncertainty sampling
+2. **Multi-agent LLM review** of difficult claims
+3. **Human verification** of the AI suggestions
+4. **Final fine-tuning of PatentSBERTa** using silver labels + gold HITL labels
+
+This workflow was designed to test whether a more advanced labeling pipeline produces stronger training data than a simple single-LLM labeling approach.
+
+## Base model
+
+The final classifier is built from:
+
+- **Base encoder:** `AI-Growth-Lab/PatentSBERTa`
+- **Task:** Binary text classification
+- **Domain:** Patent claim classification for climate mitigation / green technology
+
+## Data used in the notebook
+
+The notebook uses the following files:
+
+- `patents_50k_green.parquet`
+- `train_meta.csv`
+- `y_train.npy`
+- `eval_silver.parquet`
+- `hitl_green_100.csv`
+- `hitl_review_progress_with_llm.csv`
+- `hitl_green_gold.csv`
+- `hitl_three_agents.csv`
+
+## Methodology
+
+### 1. High-risk claim selection
+
+A set of **100 high-risk patent claims** was selected from earlier uncertainty sampling outputs. These were the most difficult / ambiguous examples for the model.
+
+### 2. Multi-agent debate system
+
+Three agents were created using `CrewAI` and an Ollama-hosted model (`qwen2.5:3b-instruct`):
+
+- **Advocate Agent** – argues why the claim should be classified as green under Y02 climate mitigation logic
+- **Skeptic Agent** – argues why the claim may not qualify and checks for weak evidence or greenwashing
+- **Judge Agent** – reviews both sides and returns a structured final output with:
+  - predicted label
+  - confidence
+  - rationale
+
+This produces an AI suggestion for each difficult claim.
+
+### 3. Human-in-the-Loop review
+
+The AI-generated suggestion was then manually reviewed.
+
+The final human label was stored as:
+
+- `is_green_human`
+
+These human-reviewed labels form the **gold dataset** for the difficult claims.
+
+### 4. Gold-enhanced training
+
+The final training set combines:
+
+- **Silver labels** from the earlier training data
+- **100 gold human-reviewed claims** from the multi-agent workflow
+
+This combined dataset was used to fine-tune PatentSBERTa.
+
+## Training configuration
+
+The notebook fine-tunes the model with the following setup:
+
+- **Model:** `AI-Growth-Lab/PatentSBERTa`
+- **Max sequence length:** `256`
+- **Epochs:** `1`
+- **Learning rate:** `2e-5`
+- **Train batch size:** `8`
+- **Eval batch size:** `8`
+- **Weight decay:** `0.01`
+- **Framework:** Hugging Face Transformers Trainer
+
+## Dataset splits used during fine-tuning
+
+From the notebook:
+
+- **Training data:** silver training set + gold HITL labels
+- **Evaluation data:** `eval_silver`
+- **Additional check:** `gold_100`
+
+The notebook text states that the final training dataset contains **35,200 claims**.
+
+## Human vs AI agreement
+
+According to the notebook:
+
+- **Simple LLM from Assignment 2:** `94%` agreement with human labels
+- **Agentic system from Assignment 3:** `87%` agreement with human labels
+
+This suggests that the multi-agent system used stricter reasoning criteria, which created more disagreement on borderline cases.
+
+## Repository contents
+
+Depending on what you upload, this repository may include:
+
+- the processed HITL dataset
+- the final trained model
+- tokenizer files
+- training notebook
+- prediction / rationale outputs for the 100 reviewed claims
+
+## Expected columns in the HITL dataset
+
+The notebook shows or creates columns such as:
+
+- `id`
+- `text`
+- `p_green`
+- `u`
+- `llm_green_suggested`
+- `llm_confidence`
+- `llm_rationale`
+- `is_green_human`
+
+## Example use
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model_name = "YOUR_HF_REPO_NAME"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+text = "A company develops a carbon capture system that reduces CO2 emissions from cement factories."
+inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=256)
+
+with torch.no_grad():
+    logits = model(**inputs).logits
+    pred = torch.argmax(logits, dim=-1).item()
+
+print("Predicted label:", pred)
+```
+
+## Intended use
+
+This project is intended for:
+
+- research and coursework on green patent detection
+- experimentation with HITL labeling pipelines
+- comparison of simple vs advanced AI-assisted annotation workflows
+- climate-tech related document classification
+
+## Limitations
+
+- The gold set is relatively small (**100 reviewed claims**)
+- The multi-agent workflow depends on LLM reasoning quality
+- Agreement with humans does not automatically guarantee better downstream model performance
+- Final performance metrics should be reported from the actual training run in this repository
+
+## Notes
+
+This README was prepared from the notebook workflow and code structure. If you are uploading the **model repo**, add the final evaluation metrics from your training output. If you are uploading the **dataset repo**, you can keep the methodology sections and remove the model inference example if not needed.
+
+## Citation
+
+If you use this work, please cite the repository and the base model:
+
+- `AI-Growth-Lab/PatentSBERTa`
+
+You may also describe the workflow as:
+
+> Multi-Agent Human-in-the-Loop green patent detection using PatentSBERTa with gold-enhanced fine-tuning.