TabBench: Tabular Embedding Benchmark

A Comprehensive Evaluation Suite for Tabular Embedding Models

---

Overview

TabBench is a comprehensive benchmark designed to evaluate the tabular understanding capability of embedding models. It assesses two critical dimensions of tabular representation: linear separability (via classification) and semantic alignment (via retrieval).

TabBench aggregates diverse datasets from four authoritative repositories and provides a standardized evaluation pipeline.

Benchmark Statistics

Category	Count	Samples / Corpus
Classification
Grinsztajn	56 datasets	521,889
OpenML-CC18	66 datasets	249,939
OpenML-CTR23	34 datasets	210,026
UniPredict	155 datasets	386,618
Classification Total	311 datasets	1,368,472
Retrieval
Corpus	—	1,394,247
Numeric Queries	10,000	—
Categorical Queries	10,000	—
Mixed Queries	10,000	—
Retrieval Total	30,000 queries	1,394,247

Data Format

Serialization

All tabular rows are serialized into natural language using the template:

The {column_name} is {value}. The {column_name} is {value}. ...

For example:

The age is 25. The occupation is Engineer. The salary is 75000.50. The city is New York.

Classification Task

Each dataset directory contains:

• train.jsonl / test.jsonl: Each line is a JSON object with the following fields:
  • text: Serialized tabular row
  • label: Target label (string)
  • dataset: Dataset name
  • benchmark: Source benchmark name
  • task_type: Task type (clf)
• train.csv / test.csv: Original tabular data in CSV format
• metadata.json: Dataset metadata including dataset, benchmark, sub_benchmark, task_type, data_type, target_column, label_values, num_labels, train_samples, test_samples, train_label_distribution, test_label_distribution

{"text": "The age is 36. The workclass is Private. The fnlwgt is 172256.0. ...", "label": ">50K", "dataset": "adult", "benchmark": "openml_cc18", "task_type": "clf"}

Retrieval Task

The retrieval directory contains:

• corpus.jsonl: Global corpus of serialized rows (~1.4M documents), each with fields idx, text, label, dataset, benchmark
• queries.jsonl: All retrieval queries (30,000 total: 10k numeric + 10k categorical + 10k mixed)

Corpus format:

{"idx": 0, "text": "The V1 is 3.0. The V2 is 559.0. ...", "label": "1.0", "dataset": "albert", "benchmark": "grinsztajn"}

Query format:

{
  "task": "retrieval",
  "query_id": "retrieval_numeric_000001",
  "query_text": "find records where Easter is 0",
  "query_type": "numeric",
  "conditions": [{"field": "Easter", "operator": "==", "value": 0.0, "type": "numeric"}],
  "num_conditions": 1,
  "matching_indices": [1384050, 1384051, ...],
  "num_matches": 1822
}

Evaluation Protocol

Classification (Linear Probing)

1. Extract frozen embeddings for all samples using the target model
2. Train an independent Logistic Regression classifier per dataset (max_iter=1000, random_state=42)
3. Report Accuracy and Macro-F1 on the test split

Retrieval (Dense Retrieval)

1. Encode all corpus documents and queries
2. Build a Faiss IndexFlatIP index (cosine similarity via L2-normalized vectors)
3. Retrieve top-k documents for each query
4. Report MRR@10 and nDCG@10

Overall Score

The Overall metric is the macro-average of Accuracy, F1, MRR@10, and nDCG@10.

Leaderboard

Model	#Params	Overall	Accuracy	F1	MRR@10	nDCG@10
Jina-Embeddings-v3	0.6B	41.48	60.33	46.11	32.49	26.98
Jasper-Token-Compression	0.6B	42.75	61.25	47.69	33.56	28.50
Qwen3-Embedding-0.6B	0.6B	44.92	62.81	50.32	36.00	30.56
TabEmbed-0.6B	0.6B	65.27	67.16	56.56	71.72	65.64
F2LLM-4B	4B	48.02	64.92	52.48	40.60	34.08
Octen-Embedding-4B	4B	48.62	65.36	53.64	40.97	34.51
Qwen3-Embedding-4B	4B	48.91	65.09	52.72	42.04	35.76
TabEmbed-4B	4B	70.71	69.51	59.75	79.33	74.25
SFR-Embedding-Mistral	7B	49.42	64.28	50.75	44.23	38.41
Linq-Embed-Mistral	7B	50.74	66.06	53.33	44.65	38.92
GTE-Qwen2-7B-Instruct	7B	51.27	64.67	51.76	47.44	41.19
Qwen3-Embedding-8B	8B	48.03	65.08	52.81	40.06	34.16
TabEmbed-8B	8B	71.62	69.88	60.19	80.58	75.83

Quick Start

# Clone the evaluation code
git clone https://github.com/qiangminjie27/TabEmbed.git
cd TabEmbed
pip install -r requirements.txt

# Run evaluation
python src/run_benchmark.py \
    --benchmark_dir /path/to/TabBench \
    --model_name_or_path your-model-name \
    --output_dir results/ \
    --max_seq_length 1024 \
    --batch_size 64

Source Datasets

TabBench is built upon the following high-quality data sources:

• Grinsztajn et al. (2022) — Tree-based models benchmark
• OpenML-CC18 — OpenML curated classification benchmark
• OpenML-CTR23 — OpenML tabular regression benchmark
• UniPredict — Universal prediction benchmark

Raw evaluation data is sourced from tabula-8b-eval-suite.

Citation

If you use TabBench in your research, please cite:

@misc{qiang2026tabembedbenchmarkinglearninggeneralist,
      title={TabEmbed: Benchmarking and Learning Generalist Embeddings for Tabular Understanding}, 
      author={Minjie Qiang and Mingming Zhang and Xiaoyi Bao and Xing Fu and Yu Cheng and Weiqiang Wang and Zhongqing Wang and Ningtao Wang},
      year={2026},
      eprint={2605.04962},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2605.04962}, 
}

License

This benchmark is released under the MIT License.

Note: The individual upstream datasets included in this benchmark may have their own respective licenses. Please refer to the original data sources for their specific terms.