no code implementations • 26 Feb 2024 • Isabelle Mohr, Markus Krimmel, Saba Sturua, Mohammad Kalim Akram, Andreas Koukounas, Michael Günther, Georgios Mastrapas, Vinit Ravishankar, Joan Fontanals Martínez, Feng Wang, Qi Liu, Ziniu Yu, Jie Fu, Saahil Ognawala, Susana Guzman, Bo wang, Maximilian Werk, Nan Wang, Han Xiao
We introduce a novel suite of state-of-the-art bilingual text embedding models that are designed to support English and another target language.
2 code implementations • 30 Oct 2023 • Michael Günther, Jackmin Ong, Isabelle Mohr, Alaeddine Abdessalem, Tanguy Abel, Mohammad Kalim Akram, Susana Guzman, Georgios Mastrapas, Saba Sturua, Bo wang, Maximilian Werk, Nan Wang, Han Xiao
Text embedding models have emerged as powerful tools for transforming sentences into fixed-sized feature vectors that encapsulate semantic information.
no code implementations • 20 Jul 2023 • Michael Günther, Louis Milliken, Jonathan Geuter, Georgios Mastrapas, Bo wang, Han Xiao
Jina Embeddings constitutes a set of high-performance sentence embedding models adept at translating textual inputs into numerical representations, capturing the semantics of the text.
no code implementations • 24 Jan 2022 • Michael Günther, Andreas Brendel, Walter Kellermann
In this contribution, we provide a comprehensive analysis of model-based microphone utility estimation approaches that use signal features and, as an alternative, also propose machine learning-based estimation methods that identify optimal sensor signal utility features.
2 code implementations • 1 Apr 2020 • Christoph Günther, Michael Günther, Daniel Günther
The control of the COVID-19 pandemic requires a considerable reduction of contacts mostly achieved by imposing movement control up to the level of enforced quarantine.
Social and Information Networks
no code implementations • 28 Nov 2019 • Michael Günther, Maik Thiele, Wolfgang Lehner
Thus, we argue to additionally incorporate the information given by the database schema into the embedding, e. g. which words appear in the same column or are related to each other.