update readme.md

README.md

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----
-license: mit
----
+---
+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](https://privacy-hosting.isi.edu/data/OPP-115.pdf).
+
+| 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             |
+
+---
+
+## Model Details
+
+- **Architecture**: DistilBERT (pretrained)
+- **Fine-tuning Dataset**: [OPP-115 Dataset](https://privacy-hosting.isi.edu/data/OPP-115.pdf)
+- **Input Format**: Text snippets from privacy policies
+- **Output Format**: Predicted class label with probabilities
+
+---
+
+## Intended Uses
+
+- Automatic **privacy policy clause classification**
+- **Regulatory technology (RegTech)** tools
+- **Privacy policy summarization** and simplification
+- **Risk analysis** for data sharing and collection practices
+
+---
+
+## How to Use
+
+```python
+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}")