ML Application · Python

Anonymizer-PII-STAPRO

Petr Sovadina@petrsovadina · Atlas 68.5 · #6 builder

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What it is

MedDocAI Anonymizer is a machine learning application for anonymizing personally identifiable information in medical documents, with specific support for Czech entities. It provides a web interface, REST API, and batch processing capabilities using Presidio and spaCy for named entity recognition.

Scored from the code at its last scan, 22 Oct 2026. How merit is scored

Where it counts

Machine Learning90%
NLP & LLMs85%
Data Science20%
MLOps10%

How much of the project is about each field, as scoring counts it. It ranks in the field it is most about.

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14kernel-workArchitecture Modification / Research · PythonThis repository implements a gather-matmul-scatter overlay for the `deep_chaos_scheduler` to optimize training by computing only on active channels. It includes the overlay implementation, tests for correctness, and benchmarks to measure performance impact.60.5/100AdvancedRickMachine Learning
154cast-livive-netEducational Implementation · RustThis repository implements four machine learning classifiers (centroid, k-NN, perceptron, and MLP) from scratch in Rust, with a fixed input/output contract for comparison. It includes educational tooling for data preparation, model training, evaluation, and visualization in a…60.3/100AdvancedJarad PeckenpaughMachine Learning
16Anonymizer-PII-STAPROML Application · Python59.8/100AdvancedPetr SovadinaMachine Learning
17, shared rankAurora-SDG-Publication-ClassifierML Application · PythonThe Aurora-SDG-Publication-Classifier is a Streamlit-based machine learning application that classifies academic publications according to the United Nations Sustainable Development Goals (SDGs). It integrates data from multiple sources (OpenAlex, DSpace, OAI-PMH), enriches…58.9/100AdvancedJens MittelbachMachine Learning
17, shared rankrag-pocEducational Implementation · TypeScriptThis repository is a client-side, zero-backend Retrieval-Augmented Generation (RAG) proof-of-concept that runs entirely in the browser, using PDF.js for text extraction, Transformers.js for embeddings, and web-llm for local LLM inference. It is designed as an educational tool…58.9/100AdvancedJarad PeckenpaughMachine Learning

What it is

Where it counts

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