TabEmbed: Benchmarking and Learning Generalist Embeddings for Tabular Understanding
Summary
This paper introduces TabEmbed, a generalist embedding model for tabular data that unifies classification and retrieval tasks, along with TabBench, a new benchmark for evaluating tabular understanding.
View Cached Full Text
Cached at: 05/08/26, 07:13 AM
Paper page - TabEmbed: Benchmarking and Learning Generalist Embeddings for Tabular Understanding
Source: https://huggingface.co/papers/2605.04962
Abstract
A new generalist embedding model called TabEmbed is introduced that unifies tabular classification and retrieval tasks within a shared embedding space using large-scale contrastive learning with positive-aware hard negative mining.
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 lackretrieval-compatible vector outputs, whereas text embedding models often fail to capture tabular structure and numerical semantics. To bridge this gap, we first introduce theTabular 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 unifiestabular classificationandretrievalwithin a sharedembedding space. By reformulating diverse tabular tasks assemantic matching problems, TabEmbed leverageslarge-scale contrastive learningwithpositive-aware hard negative miningto 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 foruniversal tabular representation learning. Code and datasets are publicly available at https://github.com/qiangminjie27/TabEmbed and https://huggingface.co/datasets/qiangminjie27/TabBench.
View arXiv pageView PDFGitHub1Add to collection
Get this paper in your agent:
hf papers read 2605\.04962
Don’t have the latest CLI?curl \-LsSf https://hf\.co/cli/install\.sh \| bash
Models citing this paper0
No model linking this paper
Cite arxiv.org/abs/2605.04962 in a model README.md to link it from this page.
Datasets citing this paper1
#### qiangminjie27/TabBench Preview• Updatedabout 4 hours ago • 934
Spaces citing this paper0
No Space linking this paper
Cite arxiv.org/abs/2605.04962 in a Space README.md to link it from this page.
Collections including this paper0
No Collection including this paper
Add this paper to acollectionto link it from this page.
Similar Articles
MulTaBench: Benchmarking Multimodal Tabular Learning with Text and Image
Introduces MulTaBench, a benchmark of 40 datasets for multimodal tabular learning with text and image modalities, demonstrating that task-specific embedding tuning improves performance over frozen pretrained embeddings, particularly when modalities provide complementary predictive signals.
TRL-Bench: Standardizing Cross-Paradigm Representation-Level Evaluation of Tabular Encoders
TRL-Bench is a unified framework and library for standardizing the evaluation of tabular representation learning models across 20 encoders, 16 tasks, and 87 datasets. It provides a common interface to compare heterogeneous tabular models and reveals that no single encoder is best for all tasks.
MVEB: Massive Video Embedding Benchmark
This paper introduces MVEB, a large-scale benchmark for evaluating video embeddings across 23 tasks, finding that no single model dominates and that audio's contribution depends on dataset annotation provenance. It integrates into the MTEB ecosystem for unified multimodal evaluation.
JFinTEB: Japanese Financial Text Embedding Benchmark
JFinTEB introduces the first comprehensive benchmark for evaluating Japanese financial text embeddings, addressing a gap in domain-specific and language-specific evaluation resources. The benchmark includes retrieval and classification tasks evaluated across Japanese-specific, multilingual, and commercial embedding models, with datasets and evaluation framework publicly released.
WildTableBench: Benchmarking Multimodal Foundation Models on Table Understanding In the Wild
WildTableBench introduces the first question-answering benchmark for real-world table images, revealing that existing multimodal foundation models struggle significantly with structural perception and numerical reasoning, with only one model exceeding 50% accuracy.