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Datasets:
ibm-research
/
MultiHierttRetrieval

Tasks:
Text Retrieval
Modalities:
Text
Formats:
json
Sub-tasks:
document-retrieval
Languages:
English
Size:
10K - 100K
Tags:
table-retrieval
text
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MultiHierttRetrieval
File size: 3,413 Bytes 
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---
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: MultiHierTT
config_names:
  - default
  - queries
  - corpus

dataset_info:
  - config_name: default
    features:
      - name: qid
        dtype: string
      - name: did
        dtype: string
      - name: score
        dtype: int32
    splits:
      - name: dev
        num_bytes: 45922
        num_examples: 1068
  - config_name: queries
    features:
      - name: _id
        dtype: string
      - name: text
        dtype: string
    splits:
      - name: dev_queries
        num_bytes: 212136
        num_examples: 929
  - config_name: corpus
    features:
      - name: _id
        dtype: string
      - name: title
        dtype: string
      - name: text
        dtype: string
    splits:
      - name: corpus
        num_bytes: 33851099
        num_examples: 9902

configs:
  - config_name: default
    data_files:
      - split: dev
        path: dev_qrels.jsonl
  - config_name: queries
    data_files:
      - split: dev_queries
        path: dev_queries.jsonl
  - config_name: corpus
    data_files:
      - split: corpus
        path: corpus.jsonl
---

# MultiHierTT Retrieval

This dataset is part of a Table + Text retrieval benchmark. Includes queries and relevance judgments across dev split(s), with corpus in 1 format(s): `corpus`.

## Configs

| Config | Description | Split(s) |
|---|---|---|
| `default` | Relevance judgments (qrels): `qid`, `did`, `score` | `dev` |
| `queries` | Query IDs and text | `dev_queries` |
| `corpus` | Plain text corpus: `_id`, `title`, `text` | `corpus` |

## 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{zhao-etal-2022-multihiertt,
    title = "{M}ulti{H}iertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data",
    author = "Zhao, Yilun  and
      Li, Yunxiang  and
      Li, Chenying  and
      Zhang, Rui",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.acl-long.454",
    pages = "6588--6600",
}
```