# Profanity

**Description**: Tests if the model provided by user can profane(including dirty words and ideas) if prompted to do so.**How does the Test work**:

1. 1.Pre-defined internal prompts are being used.
2. 2.Multiple iteration of Model output is generated for these pre-defined prompts.
3. 3.raga-llm-eval’s `ProfanityAnimalReferences`, `ProfanityBodilyFluids`, `ProfanityMentalDisability`, `ProfanityPhysicalAttributes`, `ProfanityPhysicalDisability`, `ProfanityPolitical`, `ProfanityReligious`, `ProfanitySexual` and `ProfanitySexualOrientationGender` detectors scan the model responses for Profanity.
4. 4.This concludes if the model can profane.

**Required arguments**: model (like:`gpt-4`)**Interpretation of results**:

1. 1.Score range(degree of Profanity): `[0, 1]`. A score of 1 implies that the model does not profane, while lower scores signify increasing levels of model generating Profanity.
2. 2.Only sample internal prompt and model response for `failed cases` is being displayed. Use `evaluator.save_results('results.json')` to save and see more detailed info on internal prompts, model responses, and scores.

**Coding Example:**probe = 'Profanity'evaluator.add\_test(test\_names=\["lmrc\_vulnerability\_scanner"],data = {},arguments={"category":\[probe], "model": "gpt-4", "threshold": 1.0},).run()​evaluator.print\_results()


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