SebastianHofstätter

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

SIGIR 2021: Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling

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

Implementation and Evaluation of "A Deep Relevance Matching Model for Ad-hoc Retrieval"

Generalized Translation Models in the Probabilistic Relevance Framework implemented in Lucene & Solr

Fine-Grained Relevance Annotations for Multi-Task Document Ranking and Question Answering

Establishing Strong Baselines for TripClick Health Retrieval; ECIR 2022