---
language: en
license: apache-2.0
library_name: transformers
tags:
  - bert
  - text-classification
  - privacy-policy
  - gdpr
  - torchscript
datasets:
  - MAPP-116
metrics:
  - f1
model-index:
  - name: PARENT BERT
    results:
      - task:
          type: text-classification
        dataset:
          name: MAPP-116
          type: text
        metrics:
          - name: f1
            type: score
            value: 0.80  # replace with your actual F1 score
---




# PARENT BERT Models for Privacy Policy Analysis

This repository contains **TorchScript versions of 15 fine-tuned BERT models** used in the PARENT project to analyse mobile app privacy policies. These models identify **what data is collected, why it is collected, and how it is processed**, helping assess GDPR compliance.  

They are part of a hybrid framework designed for non-technical users, particularly parents concerned about children’s privacy.

---

## Model Purpose

- Segment privacy policies to detect:
  - Data collection types (e.g., contact info, location)
  - Purpose of data collection
  - How data is processed
- Support GDPR compliance evaluation
- Detect potential third-party sharing (in combination with a logistic regression model)

---
##  References

- **MAPP Dataset:** Arora, S., Hosseini, H., Utz, C., Bannihatti Kumar, V., Dhellemmes, T., Ravichander, A., Story, P., Mangat, J., Chen, R., Degeling, M., Norton, T.B., Hupperich, T., Wilson, S., & Sadeh, N.M. (2022). *A tale of two regulatory regimes: Creation and analysis of a bilingual privacy policy corpus*. Proceedings of the International Conference on Language Resources and Evaluation (LREC 2022). [PDF link](https://aclanthology.org/2022.lrec-1.585.pdf) [Accessed 12 July 2025].
---

##  Usage

```python
import torch
from transformers import BertTokenizerFast
from huggingface_hub import hf_hub_download

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
REPO_ID = "Bnaad/PARENT_bert"

# Load tokenizer
tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")

# Load one TorchScript model from Hugging Face
label_name = "Information Type_Contact information"
safe_label = label_name.replace(" ", "_").replace("/", "_")
filename = f"torchscript_{safe_label}.pt"
model_path = hf_hub_download(repo_id=REPO_ID, filename=filename)
model = torch.jit.load(model_path, map_location=device)
model.to(device)
model.eval()

# Example inference
sample_text = """For any questions about your account or our services, please contact our customer support team by emailing support@example.com, calling +1-800-555-1234, or visiting our office at 123 Main Street, Springfield, IL, 62701 during business hours"""
inputs = tokenizer(
    sample_text, 
    return_tensors="pt", 
    truncation=True, 
    padding="max_length", 
    max_length=512
).to(device)

with torch.no_grad():
    outputs = model(inputs["input_ids"], inputs["attention_mask"])
    
print("Logits:", outputs)
prob = torch.sigmoid(outputs.squeeze())
print(prob)