Upload README_hf_model.md

README_hf_model.md

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+---
+license: mit
+language:
+  - en
+library_name: transformers
+pipeline_tag: text-classification
+tags:
+  - patents
+  - climate-tech
+  - green-patents
+  - patentsberta
+  - classification
+  - academic-project
+---
+
+# PatentSBERTa Green Patent Classifier (Silver + Gold + MAS + HITL)
+
+This repository contains the **final fine-tuned PatentSBERTa model** developed for the *Advanced Agentic Workflow with QLoRA* final project.
+
+The model classifies **patent claims as green vs non-green technologies**, focusing on climate mitigation technologies aligned with **CPC Y02 classifications**.
+
+The training pipeline combines **silver labels, agent debate labeling, and targeted human review** to improve classification quality on difficult claims.
+
+---
+
+## Model Overview
+
+**Base model:** `AI-Growth-Lab/PatentSBERTa`  
+**Task:** Binary classification  
+**Labels:**
+
+| Label | Meaning |
+| --- | --- |
+| 0 | Non-green technology |
+| 1 | Green technology (climate mitigation related) |
+
+The model is fine-tuned using HuggingFace `AutoModelForSequenceClassification`.
+
+---
+
+## Training Data
+
+The training dataset is based on a **balanced 50k patent claim dataset** derived from:
+
+`AI-Growth-Lab/patents_claims_1.5m_train_test`
+
+Dataset composition:
+
+| Source | Description |
+| --- | --- |
+| Silver Labels | Automatically derived from CPC Y02 indicators |
+| Gold Labels | 100 high-uncertainty claims reviewed using MAS + Human-in-the-Loop |
+
+Final training set:
+
+`train_silver + gold_100`
+
+The **gold dataset overrides the silver labels** for those claims to improve supervision on ambiguous cases.
+
+---
+
+## Pipeline Architecture
+
+The full system used in the project consists of several stages:
+
+1. **Baseline Model**
+  - Frozen PatentSBERTa embeddings
+  - Logistic Regression classifier
+
+2. **Uncertainty Sampling**
+  - Identifies 100 claims with highest prediction uncertainty
+
+3. **QLoRA Domain Adaptation**
+  - LLM fine-tuned to better understand patent language
+
+4. **Multi-Agent System (MAS)**
+  - Advocate agent: argues claim is green
+  - Skeptic agent: argues claim is not green
+  - Judge agent: decides final classification
+
+5. **Targeted Human Review**
+  - Human only reviews cases where MAS confidence is low or agents disagree
+
+6. **Final Model Training**
+  - PatentSBERTa fine-tuned on silver data + gold labels
+
+---
+
+## Evaluation
+
+The model is evaluated on the **eval_silver split** of the dataset.
+
+Primary metric:
+
+**F1 score**
+
+Additional metrics reported:
+
+- Precision
+- Recall
+- Accuracy
+- Confusion Matrix
+
+The evaluation script also exports prediction probabilities for analysis.
+
+---
+
+## Usage
+
+Example inference using HuggingFace Transformers:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model_name = "YOUR_USERNAME/BDS_M4_exam_final_model"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+text = "A system for capturing carbon emissions using advanced filtration..."
+
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)
+
+with torch.no_grad():
+    outputs = model(**inputs)
+
+prob_green = torch.softmax(outputs.logits, dim=-1)[0,1].item()
+
+print("Probability green:", prob_green)
+```
+
+## Limitations
+
+- Silver labels are derived from CPC Y02 classifications and may contain noise.
+- Only 100 claims were manually reviewed, meaning supervision improvements are limited to high-uncertainty cases.
+- Patent claims can be extremely technical and ambiguous, which may impact classification accuracy.
+
+## Project Context
+
+This model was developed as part of the M4 Advanced AI Systems final assignment.  
+The project explores agentic workflows for data labeling, combining:
+
+- QLoRA fine-tuning
+- Multi-Agent Systems
+- Human-in-the-Loop review
+- Transformer fine-tuning
+
+## Citation
+
+If referencing this model in academic work:
+
+**Green Patent Detection with Agentic Workflows.**  
+**M4 Advanced AI Systems Final Project.**
+
+## Authors
+
+Student project submission by Daniel Hjerresen.