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Datasets:
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TabBench

Tasks:
Text Classification
Feature Extraction
Text Retrieval
Modalities:
Text
Tabular
Formats:
json
Languages:
English
Size:
1M - 10M
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Tags:
tabular
embedding
benchmark
contrastive-learning
retrieval
classification
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metadata 
language:
  - en
license: mit
size_categories:
  - 1M<n<10M
task_categories:
  - text-classification
  - feature-extraction
  - text-retrieval
tags:
  - tabular
  - embedding
  - benchmark
  - contrastive-learning
  - retrieval
  - classification
pretty_name: TabBench - Tabular Embedding Benchmark

TabBench: Tabular Embedding Benchmark

A Comprehensive Evaluation Suite for Tabular Embedding Models

GitHub

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:

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

Citation

If you use TabBench in your research, please cite:

Paper coming soon. Please check back later for the BibTeX citation.

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.