Label	Description
`REAL_DATA`	Genuine sensitive data (PII, credentials, secrets)
`TEST_DATA`	Synthetic/mock data used in tests or examples
`DOCUMENTATION`	Documentation examples, tutorials, placeholder values
`NOISE`	Random strings, hashes, or non-sensitive matches

Base model: sentence-transformers/all-MiniLM-L6-v2
Framework: SetFit (few-shot fine-tuning)
Test accuracy: 94.25%

Usage

python

from setfit import SetFitModel

model = SetFitModel.from_pretrained("Sumeetgpt/dlp-intent-classifier")
predictions = model.predict(["sk-proj-abc123XYZ", "test_api_key_placeholder"])
print(predictions)  # ['REAL_DATA', 'TEST_DATA']

Intended Use

Used inside the Spidercob DLP engine to verify whether regex-matched findings are genuinely sensitive before blocking or alerting. Blocks on REAL_DATA confidence > 0.7; allows through on TEST_DATA confidence > 0.8.

Training Data

Trained on curated examples spanning:

Real PII samples (anonymized): emails, SSNs, credit cards, API keys, passwords
Synthetic test data from faker, factory_boy, pytest fixtures
Documentation examples from popular libraries
Noise patterns (random strings, base64, UUIDs)

Limitations

Optimized for English text
Best performance on short text snippets (< 512 tokens)
Not a replacement for regex-based pattern matching — meant as a downstream filter