首个统一表格分类与检索的通用嵌入模型，解决结构与数值语义理解难题。
TabEmbed: Benchmarking and Learning Generalist Embeddings for Tabular Understanding
- 将表格任务转为语义匹配，用对比学习捕捉细粒度结构与数值特征。
- 在TabBench上显著超越现有文本嵌入模型，建立新基准。
- 适合需要统一处理表格数据的AI研发人员与数据科学家。
基础模型已在自然语言处理中建立统一表征，但表格数据领域仍缺乏类似范式。现有方法存在根本局限：基于大语言模型的方法缺乏可检索的向量输出，而文本嵌入模型常无法捕捉表格结构与数值语义。为此，我们首先提出表格嵌入基准（TabBench），一个全面评估嵌入模型表格理解能力的综合套件。随后，我们提出TabEmbed，首个将表格分类与检索统一于共享嵌入空间的通用嵌入模型。通过将多样表格任务重构为语义匹配问题，TabEmbed利用大规模对比学习与正样本感知的困难负样本挖掘，精确识别细微的结构与数值差异。在TabBench上的实验表明，TabEmbed显著优于当前最优文本嵌入模型，确立了通用表格表示学习的新基准。代码与数据集已公开于https://github.com/qiangminjie27/TabEmbed和https://huggingface.co/datasets/qiangminjie27/TabBench。
原文摘要 · Abstract (English)
Foundation models have established unified representations for natural language processing, yet this paradigm remains largely unexplored for tabular data. Existing methods face fundamental limitations: LLM-based approaches lack retrieval-compatible vector outputs, whereas text embedding models often fail to capture tabular structure and numerical semantics. To bridge this gap, we first introduce the Tabular Embedding Benchmark (TabBench), a comprehensive suite designed to evaluate the tabular understanding capability of embedding models. We then propose TabEmbed, the first generalist embedding model that unifies tabular classification and retrieval within a shared embedding space. By reformulating diverse tabular tasks as semantic matching problems, TabEmbed leverages large-scale contrastive learning with positive-aware hard negative mining to discern fine-grained structural and numerical nuances. Experimental results on TabBench demonstrate that TabEmbed significantly outperforms state-of-the-art text embedding models, establishing a new baseline for universal tabular representation learning. Code and datasets are publicly available at https://github.com/qiangminjie27/TabEmbed and https://huggingface.co/datasets/qiangminjie27/TabBench.
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