Sebastian Hofstätter

teaching. Open-Source Information Retrieval Courses @ TU Wien

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

neural-ranking-kd. Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation

tas-balanced-dense-retrieval. SIGIR 2021: Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling

sigir19-neural-ir. Source code for: On the Effect of Low-Frequency Terms on Neural-IR Models, SIGIR'19

colberter. Jupyter Notebook

neural-ranking-drmm. Implementation and Evaluation of "A Deep Relevance Matching Model for Ad-hoc Retrieval"

neural-ir-explorer. Neural-IR-Explorer: A Content-Focused Tool to Explore Neural Re-Ranking Results

intra-document-cascade. Jupyter Notebook

ir-generalized-translation-models. Generalized Translation Models in the Probabilistic Relevance Framework implemented in Lucene & Solr

fira-trec-19-dataset. Fine-Grained Relevance Annotations for Multi-Task Document Ranking and Question Answering

search-engine. Prototype of a super-fast search engine for information retrieval tasks using .NET Core

tripclick. Establishing Strong Baselines for TripClick Health Retrieval; ECIR 2022

embedding_vector_similarity. Api and library for getting related terms from a word embedding