README.md

metadata

license: mit
tags:
  - privacy
  - policy-analysis
  - classification
  - text-classification
  - transformers
  - distilbert
library_name: transformers
datasets:
  - opp-115
model-index:
  - name: Privacy Clause Classifier (DistilBERT - OPP-115)
    results: []

Privacy Clause Classifier (DistilBERT - OPP-115)

This model is a fine-tuned DistilBERT model designed to classify privacy policy clauses into one of the predefined privacy practices based on the OPP-115 dataset.

ID	Category
0	Data Retention
1	Data Security
2	Do Not Track
3	First Party Collection/Use
4	International and Specific Audiences
5	Other
6	Policy Change
7	Third Party Sharing/Collection
8	User Access, Edit and Deletion
9	User Choice/Control

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Model Details

• Architecture: DistilBERT (pretrained)
• Fine-tuning Dataset: OPP-115 Dataset
• Input Format: Text snippets from privacy policies
• Output Format: Predicted class label with probabilities

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Intended Uses

• Automatic privacy policy clause classification
• Regulatory technology (RegTech) tools
• Privacy policy summarization and simplification
• Risk analysis for data sharing and collection practices

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How to Use

from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification
import torch

# Load model
tokenizer = DistilBertTokenizerFast.from_pretrained("your-hf-username/your-model-name")
model = DistilBertForSequenceClassification.from_pretrained("your-hf-username/your-model-name")

# Predict
text = "We may collect your location data to provide customized services."
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
outputs = model(**inputs)
predicted_class = torch.argmax(outputs.logits, dim=-1).item()

print(f"Predicted Category: {predicted_class}")