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cs.CL2025
MMTEB: Massive Multilingual Text Embedding Benchmark
Kenneth Enevoldsen, Isaac Chung, Imene Kerboua +83
Text embeddings are typically evaluated on a limited set of tasks, which are constrained by language, domain, and task diversity. To address these limitations and provide a more co…
cs.CL2024★ 2 cited
German Text Embedding Clustering Benchmark
Silvan Wehrli, Bert Arnrich, Christopher Irrgang
This work introduces a benchmark assessing the performance of clustering German text embeddings in different domains. This benchmark is driven by the increasing use of clustering n…