We introduce JFinTEB, the first comprehensive benchmark specifically designed for evaluating Japanese financial text embeddings. Existing embedding benchmarks provide limited coverage of language-specific and domain-specific aspects found in Japanese financial texts. Our benchmark encompasses diverse task categories including retrieval and classification tasks that reflect realistic and well-defined financial text processing scenarios. The retrieval tasks leverage instruction-following datasets and financial text generation queries, while classification tasks cover sentiment analysis, document categorization, and domain-specific classification challenges derived from economic survey data. We conduct extensive evaluations across a wide range of embedding models, including Japanese-specific models of various sizes, multilingual models, and commercial embedding services. We publicly release JFinTEB datasets and evaluation framework at https://github.com/retarfi/JFinTEB to facilitate future research and provide a standardized evaluation protocol for the Japanese financial text mining community. This work addresses a critical gap in Japanese financial text processing resources and establishes a foundation for advancing domain-specific embedding research.
JFinTEB: Japanese Financial Text Embedding Benchmark
JFinTEB provides the first comprehensive benchmark for evaluating Japanese financial text embeddings, covering retrieval and classification tasks with diverse financial text processing scenarios.
- Year
- 2026
- Venue
- arXiv 2026
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- 2
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- Abstract onlyARXIV-DEFAULT
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- arxiv.org/abs/2604.15882ARXIV-DEFAULT
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