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--- |
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language: en |
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license: apache-2.0 |
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library_name: transformers |
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tags: |
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- bert |
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- text-classification |
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- privacy-policy |
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- gdpr |
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- torchscript |
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datasets: |
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- MAPP-116 |
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metrics: |
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- f1 |
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model-index: |
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- name: PARENT BERT |
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results: |
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- task: |
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type: text-classification |
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dataset: |
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name: MAPP-116 |
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type: text |
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metrics: |
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- name: f1 |
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type: score |
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value: 0.80 |
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--- |
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# PARENT BERT Models for Privacy Policy Analysis |
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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. |
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They are part of a hybrid framework designed for non-technical users, particularly parents concerned about children’s privacy. |
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--- |
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## Model Purpose |
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- Segment privacy policies to detect: |
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- Data collection types (e.g., contact info, location) |
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- Purpose of data collection |
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- How data is processed |
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- Support GDPR compliance evaluation |
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- Detect potential third-party sharing (in combination with a logistic regression model) |
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--- |
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## References |
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- **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]. |
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--- |
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## Usage |
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```python |
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import torch |
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from transformers import BertTokenizerFast |
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from huggingface_hub import hf_hub_download |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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REPO_ID = "Bnaad/PARENT_bert" |
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# Load tokenizer |
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tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased") |
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# Load one TorchScript model from Hugging Face |
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label_name = "Information Type_Contact information" |
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safe_label = label_name.replace(" ", "_").replace("/", "_") |
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filename = f"torchscript_{safe_label}.pt" |
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model_path = hf_hub_download(repo_id=REPO_ID, filename=filename) |
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model = torch.jit.load(model_path, map_location=device) |
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model.to(device) |
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model.eval() |
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# Example inference |
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sample_text = """For any questions about your account or our services, please contact our customer support team by emailing [email protected], calling +1-800-555-1234, or visiting our office at 123 Main Street, Springfield, IL, 62701 during business hours""" |
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inputs = tokenizer( |
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sample_text, |
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return_tensors="pt", |
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truncation=True, |
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padding="max_length", |
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max_length=512 |
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).to(device) |
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with torch.no_grad(): |
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outputs = model(inputs["input_ids"], inputs["attention_mask"]) |
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print("Logits:", outputs) |
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prob = torch.sigmoid(outputs.squeeze()) |
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print(prob) |
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