README.md

metadata

annotations_creators:
  - derived
language:
  - eng
license: cc-by-4.0
multilinguality: monolingual
task_categories:
  - text-retrieval
task_ids:
  - document-retrieval
tags:
  - table-retrieval
  - text
pretty_name: OpenWikiTables
config_names:
  - default
  - queries
  - corpus_linearized
  - corpus_md
  - corpus_structure
dataset_info:
  - config_name: default
    features:
      - name: qid
        dtype: string
      - name: did
        dtype: string
      - name: score
        dtype: int32
    splits:
      - name: test
        num_bytes: 443966
        num_examples: 8425
  - config_name: queries
    features:
      - name: _id
        dtype: string
      - name: text
        dtype: string
    splits:
      - name: test_queries
        num_bytes: 916628
        num_examples: 6602
  - config_name: corpus_linearized
    features:
      - name: _id
        dtype: string
      - name: title
        dtype: string
      - name: text
        dtype: string
    splits:
      - name: corpus_linearized
        num_bytes: 37689839
        num_examples: 54282
  - config_name: corpus_md
    features:
      - name: _id
        dtype: string
      - name: title
        dtype: string
      - name: text
        dtype: string
    splits:
      - name: corpus_md
        num_bytes: 47610671
        num_examples: 54282
  - config_name: corpus_structure
    features:
      - name: _id
        dtype: string
      - name: title
        dtype: string
      - name: text
        dtype: string
      - name: meta_data
        dtype: string
      - name: headers
        sequence: string
      - name: cells
        sequence: string
    splits:
      - name: corpus_structure
        num_bytes: 86193232
        num_examples: 54282
configs:
  - config_name: default
    data_files:
      - split: test
        path: test_qrels.jsonl
  - config_name: queries
    data_files:
      - split: test_queries
        path: test_queries.jsonl
  - config_name: corpus_linearized
    data_files:
      - split: corpus_linearized
        path: corpus_linearized.jsonl
  - config_name: corpus_md
    data_files:
      - split: corpus_md
        path: corpus_md.jsonl
  - config_name: corpus_structure
    data_files:
      - split: corpus_structure
        path: corpus_structure.jsonl

OpenWikiTables Retrieval

This dataset is part of a Table + Text retrieval benchmark. Includes queries and relevance judgments across test split(s), with corpus in 3 format(s): corpus_linearized, corpus_md, corpus_structure.

Configs

Config	Description	Split(s)
default	Relevance judgments (qrels): qid, did, score	test
queries	Query IDs and text	test_queries
corpus_linearized	Linearized table representation	corpus_linearized
corpus_md	Markdown table representation	corpus_md
corpus_structure	Structured corpus with headers, cells, meta_data. text field corresponds to linearized Text + Table.	corpus_structure

corpus_structure additional fields

Field	Type	Description
meta_data	string	Table metadata / caption
headers	list[string]	Column headers
cells	list[string]	Flattened cell values

TableIR Benchmark Statistics

Dataset	Structured	#Train	#Dev	#Test	#Corpus
OpenWikiTables	✓	53.8k	6.6k	6.6k	24.7k
NQTables	✓	9.6k	1.1k	1k	170k
FeTaQA	✓	7.3k	1k	2k	10.3k
OTT-QA (small)	✓	41.5k	2.2k	--	8.8k
MultiHierTT	✗	--	929	--	9.9k
AIT-QA	✗	--	--	515	1.9k
StatcanRetrieval	✗	--	--	870	5.9k
watsonxDocsQA	✗	--	--	30	1.1k

Citation

If you use TableIR Eval: Table-Text IR Evaluation Collection, please cite:

@misc{doshi2026tableir,
  title        = {TableIR Eval: Table-Text IR Evaluation Collection},
  author       = {Doshi, Meet and Boni, Odellia and Kumar, Vishwajeet and Sen, Jaydeep and Joshi, Sachindra},
  year         = {2026},
  institution  = {IBM Research},
  howpublished = {https://huggingface.co/collections/ibm-research/table-text-ir-evaluation},
  note         = {Hugging Face dataset collection}
}

All credit goes to original authors. Please cite their work:

@inproceedings{kweon-etal-2023-open,
    title = "Open-{W}iki{T}able : Dataset for Open Domain Question Answering with Complex Reasoning over Table",
    author = "Kweon, Sunjun  and
      Kwon, Yeonsu  and
      Cho, Seonhee  and
      Jo, Yohan  and
      Choi, Edward",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.526/",
    doi = "10.18653/v1/2023.findings-acl.526",
    pages = "8285--8297",
    abstract = "Despite recent interest in open domain question answering (ODQA) over tables, many studies still rely on datasets that are not truly optimal for the task with respect to utilizing structural nature of table. These datasets assume answers reside as a single cell value and do not necessitate exploring over multiple cells such as aggregation, comparison, and sorting. Thus, we release Open-WikiTable, the first ODQA dataset that requires complex reasoning over tables. Open-WikiTable is built upon WikiSQL and WikiTableQuestions to be applicable in the open-domain setting. As each question is coupled with both textual answers and SQL queries, Open-WikiTable opens up a wide range of possibilities for future research, as both reader and parser methods can be applied. The dataset is publicly available."
}