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fixing the readme a bit

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rushil-thareja 2025-12-25 09:23:10 +04:00
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@ -97,6 +97,7 @@ tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct")
# Initialize Tagger
tagger = Tagger(api_key="your_api_key")
tagger.set_model("gpt-oss-120b") # Strong extraction model
tagger.set_constitution("LEGAL") # Options: LEGAL, HEALTH, FINANCE
```
@ -105,7 +106,7 @@ tagger.set_constitution("LEGAL") # Options: LEGAL, HEALTH, FINANCE
The library applies differential privacy only to segments marked as private, allowing precise control over which content receives protection.
```python
dpf = DPFusion(model=model, tokenizer=tokenizer, tagger=tagger)
dpf = DPFusion(model=model, tokenizer=tokenizer, max_tokens=100, tagger=tagger)
# Sample document with sensitive information
document = """The applicant was born in 1973 and currently resides in
@ -113,18 +114,41 @@ Les Salles-sur-Verdon, France. In the early 1990s, a new criminal
phenomenon emerged in Denmark known as 'tax asset stripping cases'."""
# Build context with privacy annotations
dpf.add_message("system", "You're job is to re-write documents for privacy. You will be provided a document out a paraphrase that preserves privacy and doesn't leak personally identifiable information. Just output the paraphrase only, nothing else.", is_private=False)
dpf.add_message("user", document, is_private=True)
dpf.add_message("user", "I just passed the document to you, you can paraphrase it for privacy.", is_private=False)
dpf.add_message("assistant", "Here is the paraphrased document:", is_private=False)
dpf.add_message("system", "You are a helpful assistant that paraphrases text.", is_private=False)
dpf.add_message("user", document, is_private=True) # Mark sensitive content as private
dpf.add_message("system", "Now paraphrase this text for privacy", is_private=False)
dpf.add_message("assistant", "Sure, here is the paraphrase of the above text that ensures privacy:", is_private=False)
```
### Step 3: Execute Private Generation
### Step 3: Run Tagger to Build Private and Public Contexts
The tagger automatically identifies sensitive phrases and creates two parallel contexts:
```python
# Run tagger to identify and redact sensitive phrases
# Run tagger to extract PII and build redacted context
dpf.run_tagger()
# Extracted phrases: ['1973', 'Les Salles-sur-Verdon', 'early 1990s', 'tax asset stripping cases']
# View the two contexts that DP-Fusion uses:
print(dpf.private_context) # Original text with real values
print(dpf.public_context) # Redacted text with ____ placeholders
```
**Private context** (what the model sees with full information):
```
The applicant was born in 1973 and currently resides in Les Salles-sur-Verdon, France.
In the early 1990s, a new criminal phenomenon emerged in Denmark...
```
**Public context** (redacted version):
```
The applicant was born in ____ and currently resides in _________.
In the _______, a new criminal phenomenon emerged in Denmark...
```
### Step 4: Generate with Differential Privacy
```python
# Generate with differential privacy
output = dpf.generate(
alpha=2.0, # Rényi order
@ -135,7 +159,7 @@ output = dpf.generate(
print(output['text'])
```
### Step 4: Compute Privacy Guarantee
### Step 5: Compute Privacy Guarantee
The library provides two epsilon values for comprehensive privacy accounting: