README.md

metadata

language: en
license: apache-2.0
tags:
  - setfit
  - sentence-transformers
  - dlp
  - data-loss-prevention
  - pii
  - security
  - spidercob
base_model: sentence-transformers/all-MiniLM-L6-v2
metrics:
  - accuracy
model-index:
  - name: dlp-intent-classifier
    results:
      - task:
          type: text-classification
        metrics:
          - type: accuracy
            value: 0.9425

DLP Intent Classifier

Fine-tuned SetFit model for Data Loss Prevention intent classification. Part of the Spidercob DLP platform.

Model Description

This model classifies text snippets that triggered DLP regex patterns into one of four categories to reduce false positives:

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

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