metadata
language: en
license: apache-2.0
tags:
- patent
- green-technology
- text-classification
- patentsbert
- active-learning
- hitl
datasets:
- Peter512/patents-50k-green
base_model: AI-Growth-Lab/PatentSBERTa
PatentSBERTa Green Patent Classifier — Assignment 2
Binary classifier for green patent detection (Y02 CPC codes). Fine-tuned from AI-Growth-Lab/PatentSBERTa using active learning + GPT-4o-mini HITL gold labels.
Training
- Base model: AI-Growth-Lab/PatentSBERTa (MPNet-based)
- Task: Binary classification —
is_green(Y02 CPC codes) - Training data: 35,000 silver labels (CPC Y02*) + 100 gold labels (GPT-4o-mini HITL)
- HITL process: Uncertainty sampling selected 100 most uncertain pool claims; GPT-4o-mini labeled each with confidence + rationale; human simulated review with 90% agreement (10 overrides on low-confidence cases)
- Fine-tuning: 1 epoch, lr=2e-5, max_length=256, batch_size=16, fp16
Evaluation (eval_silver, 5,000 claims)
| Metric | Value |
|---|---|
| F1 | 0.8099 |
| Precision | 0.8207 |
| Recall | 0.7994 |
| Accuracy | 0.8126 |
Baseline (frozen PatentSBERTa + Logistic Regression): F1=0.7696
Usage
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
tokenizer = AutoTokenizer.from_pretrained("AI-Growth-Lab/PatentSBERTa", use_fast=False)
model = AutoModelForSequenceClassification.from_pretrained("Peter512/patentsbert-green-a2")
model.eval()
text = "A photovoltaic cell comprising a perovskite absorber layer..."
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)
with torch.no_grad():
logits = model(**inputs).logits
label = logits.argmax().item() # 0=not_green, 1=green
Dataset
- Source: AI-Growth-Lab/patents_claims_1.5m_traim_test
- Silver labels: CPC Y02* codes (is_green_silver)
- Splits: train_silver (35k), eval_silver (5k), pool_unlabeled (10k)
- Balance: 50/50 green/not-green