---
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: NQTables
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: train
        num_bytes: 1044168
        num_examples: 9594
      - name: dev
        num_bytes: 117198
        num_examples: 1068
      - name: test
        num_bytes: 103735
        num_examples: 966
  - config_name: queries
    features:
      - name: _id
        dtype: string
      - name: text
        dtype: string
    splits:
      - name: train_queries
        num_bytes: 955578
        num_examples: 9594
      - name: dev_queries
        num_bytes: 106125
        num_examples: 1068
      - name: test_queries
        num_bytes: 94603
        num_examples: 966
  - config_name: corpus_linearized
    features:
      - name: _id
        dtype: string
      - name: title
        dtype: string
      - name: text
        dtype: string
    splits:
      - name: corpus_linearized
        num_bytes: 416763646
        num_examples: 169898
  - config_name: corpus_md
    features:
      - name: _id
        dtype: string
      - name: title
        dtype: string
      - name: text
        dtype: string
    splits:
      - name: corpus_md
        num_bytes: 448109052
        num_examples: 169898
  - 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: 859992305
        num_examples: 169898

configs:
  - config_name: default
    data_files:
      - split: train
        path: train_qrels.jsonl
      - split: dev
        path: dev_qrels.jsonl
      - split: test
        path: test_qrels.jsonl
  - config_name: queries
    data_files:
      - split: train_queries
        path: train_queries.jsonl
      - split: dev_queries
        path: dev_queries.jsonl
      - 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
---

# NQTables Retrieval

This dataset is part of a Table + Text retrieval benchmark. Includes queries and relevance judgments across train, dev, 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` | `train`, `dev`, `test` |
| `queries` | Query IDs and text | `train_queries`, `dev_queries`, `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:

```bibtex
@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:
```bibtex
@inproceedings{herzig-etal-2021-open,
    title = "Open Domain Question Answering over Tables via Dense Retrieval",
    author = {Herzig, Jonathan  and
      M{\"u}ller, Thomas  and
      Krichene, Syrine  and
      Eisenschlos, Julian},
    editor = "Toutanova, Kristina  and
      Rumshisky, Anna  and
      Zettlemoyer, Luke  and
      Hakkani-Tur, Dilek  and
      Beltagy, Iz  and
      Bethard, Steven  and
      Cotterell, Ryan  and
      Chakraborty, Tanmoy  and
      Zhou, Yichao",
    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.naacl-main.43/",
    doi = "10.18653/v1/2021.naacl-main.43",
    pages = "512--519",
    abstract = "Recent advances in open-domain QA have led to strong models based on dense retrieval, but only focused on retrieving textual passages. In this work, we tackle open-domain QA over tables for the first time, and show that retrieval can be improved by a retriever designed to handle tabular context. We present an effective pre-training procedure for our retriever and improve retrieval quality with mined hard negatives. As relevant datasets are missing, we extract a subset of Natural Questions (Kwiatkowski et al., 2019) into a Table QA dataset. We find that our retriever improves retrieval results from 72.0 to 81.1 recall@10 and end-to-end QA results from 33.8 to 37.7 exact match, over a BERT based retriever."
}
```