radlab‑playground‑ui is a Streamlit‑based interactive playground that lets users explore information, graphs, browse generated news, interact with generative chat models, and monitor system status. All UI elements are localisation‑aware, so the interface can be presented in multiple languages with a single configuration step.

A hybrid Relational-GAT with a projection head that learns relation operators to transform embeddings, impute unseen node vectors, and expand search queries via relational paths.

Utilities and scripts to transform plWordNet into machine‑learning‑ready artifacts: build graphs, create balanced embedding datasets from lexical relations, split into train/test, and prepare indices for downstream retrieval tasks.

A lightweight, extensible gateway that lets you interact with any large generative model (LLM) through a clean REST API.

A modular research framework for large‑scale text analysis: it ingests heterogeneous corpora, applies multilingual preprocessing, computes dense embeddings, performs similarity‑based clustering, creates LLM‑driven abstractive summaries, and continuously updates a temporal knowledge‑graph that captures topic evolution and inter‑document relations.

A collection of helper utilities and handlers designed to simplify common machine‑learning workflows.

A Polish extractive Question‑Answering (QA) solution with utilities for mapping its token space to generative LLMs (Gemma, LLaMA, GPT‑OSS). The repo contains scripts for training the QA model, inference pipelines, a CLI to pre‑compute token‑mapping tables, and a generation helper that injects static bias from QA logits into a downstream generator.

An interactive web application for exploring and visualizing large graphs. It loads graphs serialized with Python’s pickle (NetworkX graphs), provides quick browsing of graph collections, and offers responsive visualization with cached computations for better performance.

A lightweight, configurable toolkit for unsupervised clustering of short or long texts. It builds vector embeddings for your data, discovers dense regions with HDBSCAN, optionally reduces dimensionality for visualization (e.g., t‑SNE), and exposes graph-friendly outputs for downstream exploration.