End-to-end Presidio evaluation toolkit uses Presidio, hugging face transformers, Azure Language Service, AzureML, and GitHub Action pipeline to provide a streamlined, end-to-end solution for assessing different PII detection frameworks/ models.
MIT License
Welcome to the e2e Presidio evaluation toolkit. This project contains two main components:
notebook
folder,This accelerator provides a modular end-to-end approach for evaluating different PII detection models. The project directory structure is as follows:
data_samples
: Contains the sample input data for the project.data-science/
: Contains the data science code and defined Python environment for PII evaluation.
evironment/
: Contains the predefined Conda environment file used for the project.src/
: Contains the Python source code for the project._config
: Configuration files for the project.addition_reg
: Additional regular expressions used in the project.data_generator
: Code related to synthetic data generation.experiment_tracking/
: Code related to tracking and managing experiments.mlops/
: Contains all the YAML files (CLI v2) to orchestrate the evaluation process.
components/
: Contains the individual components of the MLOps pipeline.data/
: Contains predefined datasets configuration for MLOps pipeline.environments/
: Contains environment configuration files for the MLOps pipeline.evaluation_pipeline.yml
: The defined machine learning pipeline in a YAML file.notebooks
: A series of notebook labs for PresidioREADME.md
: This file, which provides an overview and instructions for the project.This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
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