mlsanityData to 14 Oct 2026

German Text Embedding Clustering Benchmark

arXiv 2401.02709 ↗First posted 5 Jan 2024cs.CL cs.AIcited by 13

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Abstract (arXiv)

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 neural text embeddings in tasks that require the grouping of texts (such as topic modeling) and the need for German resources in existing benchmarks. We provide an initial analysis for a range of pre-trained mono- and multilingual models evaluated on the outcome of different clustering algorithms. Results include strong performing mono- and multilingual models. Reducing the dimensions of embeddings can further improve clustering. Additionally, we conduct experiments with continued pre-training for German BERT models to estimate the benefits of this additional training. Our experiments suggest that significant performance improvements are possible for short text. All code and datasets are publicly available.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 27 of its 45 references that are in the map. First posted on arXiv 5 Jan 2024.

Builds on

27 of 45 references.

References · 27

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  4. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  5. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  6. 2021SimCSE: Simple Contrastive Learning of Sentence Embeddingscited by 2,173
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  9. 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 801
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  12. 2021Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Modelscited by 463
15 more
  1. 2022Text and Code Embeddings by Contrastive Pre-Trainingcited by 361
  2. 2020How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Modelscited by 318
  3. 2018SentEval: An Evaluation Toolkit for Universal Sentence Representationscited by 294
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  10. 2021TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learningcited by 116
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  15. 2020Universal Sentence Representation Learning with Conditional Masked Language Modelcited by 34

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Citing papers per year: 2024: 3; 2025: 7; 2026: 3.

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  1. 2024LLMs are Also Effective Embedding Models: An In-depth Overviewcited by 32
  2. 2025Maintaining MTEB: Towards Long Term Usability and Reproducibility of Embedding Benchmarkscited by 8
  3. 2025HUME: Measuring the Human-Model Performance Gap in Text Embedding Taskscited by 7
  4. 2024MTEB-French: Resources for French Sentence Embedding Evaluation and Analysiscited by 6
  5. 2024BEIR-NL: Zero-shot Information Retrieval Benchmark for the Dutch Languagecited by 6
  6. 2025Out-of-the-Box Conditional Text Embeddings from Large Language Modelscited by 5
  7. 2025MTEB-NL and E5-NL: Embedding Benchmark and Models for Dutchcited by 5
  8. 2025On The Role of Pretrained Language Models in General-Purpose Text Embeddings: A Surveycited by 3
  9. 2025AfriMTEB and AfriE5: Benchmarking and Adapting Text Embedding Models for African Languagescited by 3
  10. 2025SEA-BED: How Do Embedding Models Represent Southeast Asian Languages?cited by 2
  11. 2026CLUBench: A Clustering Benchmarkcited by 1
  12. 2026ML-Embed: Inclusive and Efficient Embeddings for a Multilingual Worldcited by 0
1 more
  1. 2026MTEB-BR: A Text Embedding Benchmark for Brazilian Portuguesecited by 0