This is the RadLab Group workspace. Here you will find our publicly available repositories.
Our mission is to propagate open knowledge, software, datasets and models.
Our official website: radlab.dev.
If you want to contact us, feel free to send an email to hello@radlab.dev
Have fun and a nice day! :)
Omni‑Mask — offline Python tool that masks PESEL, NIP, phone, address, e‑mail, IBAN, ID numbers and full names in PDF, Word, Excel and text files, generates a reversible mapping key, and includes a simple Tkinter GUI. No internet connection required.
A companion repository for llm-router containing a collection of pipeline-ready plugins. Features a masking interface for anonymizing sensitive data and a guardrail system for validating input/output safety against defined policy rules.
LLM Router is a service that can be deployed on‑premises or in the cloud. It adds a layer between any application and the LLM provider. In real time it controls traffic, distributes a load among providers of a specific LLM, and enables analysis of outgoing requests from a security perspective (masking, anonymization, prohibited content).
llm_router_services provides HTTP services that implement the core functionality used by the LLM‑Router’s plugin system. The services expose guardrail and masking capabilities through Flask applications that can be called by the corresponding plugins in llm_router_plugins.
Anonymizer‑model is a lightweight classification‑based solution for automatically detecting and masking personally identifiable information (PII) in text data. The repository contains everything needed to train, evaluate, and serve the model via a simple API.
A collection of helper utilities and handlers designed to simplify common machine‑learning workflows.
The repository contains a set of tools that use llm-router as the core foundation.
A versatile Python library for loading, preprocessing, and handling a variety of textual data sources (PDF, DOCX, TXT, and raw strings) and preparing them for downstream machine‑learning tasks such as classification, sequence labeling, and generative modeling.
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