Open-Source Information Retrieval Courses @ TU Wien

Training & evaluation library for text-based neural re-ranking and dense retrieval models built with PyTorch

Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation

Unified Learned Sparse Retrieval Framework

Source code for: On the Effect of Low-Frequency Terms on Neural-IR Models, SIGIR'19

Neural-IR-Explorer: A Content-Focused Tool to Explore Neural Re-Ranking Results

Provides a minimal PyTorch implementation of SPLADE

SIGIR 2023: Adapting Learned Sparse Retrieval to Long Documents

"Exploring the Effectiveness of Multi-stage Fine-tuning for Cross-encoder Re-rankers", ECIR 2025.

"Document Quality Scoring for Web Crawling", WOWS 2025.

Beyond Redundancy: Embedding-Aware Novelty Reranking in Retrieval-Augmented Generation

Master Thesis on reproducibility and interpretability of neural ranking models

Fine-tuning of transformer-based models to predict semantically relevant outlinks in large-scale web crawling. Includes binary classification, score estimation with QualT5, and a multimodal BERT architecture integrating textual and metadata features for robust frontier prioritisation.

Controlled depth ablation of a BERT bi-encoder across training budgets and seeds on three BEIR tasks (nfcorpus, scifact, fiqa). L3–L12 is flat within seed noise at 20K steps; 80K training degrades every depth on zero-shot transfer (−45% NDCG@10 on fiqa for L12).

neural information retrieval and ranking & extractive question answering