MTEB-NL and E5-NL: Embedding Benchmark and Models for Dutch
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914039061282816 |
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| author | Banar, Nikolay Lotfi, Ehsan Van Nooten, Jens Arhiliuc, Cristina Kliocaite, Marija Daelemans, Walter |
| author_facet | Banar, Nikolay Lotfi, Ehsan Van Nooten, Jens Arhiliuc, Cristina Kliocaite, Marija Daelemans, Walter |
| contents | Recently, embedding resources, including models, benchmarks, and datasets, have been widely released to support a variety of languages. However, the Dutch language remains underrepresented, typically comprising only a small fraction of the published multilingual resources. To address this gap and encourage the further development of Dutch embeddings, we introduce new resources for their evaluation and generation. First, we introduce the Massive Text Embedding Benchmark for Dutch (MTEB-NL), which includes both existing Dutch datasets and newly created ones, covering a wide range of tasks. Second, we provide a training dataset compiled from available Dutch retrieval datasets, complemented with synthetic data generated by large language models to expand task coverage beyond retrieval. Finally, we release a series of E5-NL models compact yet efficient embedding models that demonstrate strong performance across multiple tasks. We make our resources publicly available through the Hugging Face Hub and the MTEB package. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12340 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MTEB-NL and E5-NL: Embedding Benchmark and Models for Dutch Banar, Nikolay Lotfi, Ehsan Van Nooten, Jens Arhiliuc, Cristina Kliocaite, Marija Daelemans, Walter Computation and Language Recently, embedding resources, including models, benchmarks, and datasets, have been widely released to support a variety of languages. However, the Dutch language remains underrepresented, typically comprising only a small fraction of the published multilingual resources. To address this gap and encourage the further development of Dutch embeddings, we introduce new resources for their evaluation and generation. First, we introduce the Massive Text Embedding Benchmark for Dutch (MTEB-NL), which includes both existing Dutch datasets and newly created ones, covering a wide range of tasks. Second, we provide a training dataset compiled from available Dutch retrieval datasets, complemented with synthetic data generated by large language models to expand task coverage beyond retrieval. Finally, we release a series of E5-NL models compact yet efficient embedding models that demonstrate strong performance across multiple tasks. We make our resources publicly available through the Hugging Face Hub and the MTEB package. |
| title | MTEB-NL and E5-NL: Embedding Benchmark and Models for Dutch |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2509.12340 |