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| """ | |
| Utility for generating mock data functions based on text descriptions. | |
| This utility uses a zero-shot classification model to match text descriptions | |
| to appropriate Faker functions for generating synthetic data. | |
| """ | |
| from transformers import pipeline | |
| from faker import Faker | |
| fake = Faker() | |
| # Create a dictionary of Faker functions with descriptive labels | |
| faker_functions = { | |
| "person name": fake.name, | |
| "first name": fake.first_name, | |
| "last name": fake.last_name, | |
| "email address": fake.email, | |
| "phone number": fake.phone_number, | |
| "street address": fake.street_address, | |
| "street": fake.street_address, | |
| "city name": fake.city, | |
| "state name": fake.state, | |
| "country name": fake.country, | |
| "zip code": fake.zipcode, | |
| "job title": fake.job, | |
| "company name": fake.company, | |
| "credit card number": fake.credit_card_number, | |
| "date of birth": fake.date_of_birth, | |
| "username": fake.user_name, | |
| "website url": fake.url, | |
| "paragraph text": fake.paragraph, | |
| "sentence text": fake.sentence, | |
| } | |
| def get_highest_score_functions(result, faker_functions, threshold=0.18): | |
| """ | |
| Process model results and map sequences to appropriate Faker functions. | |
| Args: | |
| result: The classification results from the model | |
| faker_functions: Dictionary mapping labels to Faker functions | |
| threshold: Minimum confidence score to assign a function (default: 0.18) | |
| Returns: | |
| dict: Mapping of input sequences to corresponding Faker functions | |
| """ | |
| sequence_to_function = {} | |
| for item in result: | |
| sequence = item["sequence"] | |
| label = item["labels"][0] | |
| score = item["scores"][0] | |
| if score >= threshold: | |
| sequence_to_function[sequence] = faker_functions.get(label) | |
| else: | |
| sequence_to_function[sequence] = None | |
| return sequence_to_function | |
| def get_functions_for_descriptions(descriptions): | |
| """ | |
| Get mock data functions based on descriptions or property names. | |
| Uses zero-shot classification to match text descriptions to appropriate | |
| Faker functions for generating synthetic data. | |
| Args: | |
| descriptions: Array of descriptions or property names to classify | |
| Returns: | |
| dict: Mapping of descriptions to corresponding mock data functions | |
| """ | |
| # Create pipeline with small zero-shot classification model | |
| pipe = pipeline(model="MoritzLaurer/DeBERTa-v3-xsmall-mnli-fever-anli-ling-binary") | |
| # Call pipeline with descriptions and available Faker function labels | |
| result = pipe(descriptions, candidate_labels=list(faker_functions.keys())) | |
| # Process results using helper function with default confidence threshold | |
| return get_highest_score_functions(result, faker_functions, threshold=0.18) | |