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Datasets:
ibm-research
/
NQTablesRetrieval

Tasks:
Text Retrieval
Modalities:
Text
Formats:
json
Sub-tasks:
document-retrieval
Languages:
English
Size:
100K - 1M
Tags:
table-retrieval
text
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
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Files changed (26) hide show
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  2. README.md +209 -3
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  26. train_structure/train.jsonl.gz +3 -0
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+ corpus_structure.jsonl filter=lfs diff=lfs merge=lfs -text
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- ---
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- license: apache-2.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ annotations_creators:
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+ - derived
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+ language:
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+ - eng
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+ license: cc-by-4.0
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+ multilinguality: monolingual
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+ task_categories:
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+ - text-retrieval
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+ task_ids:
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+ - document-retrieval
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+ tags:
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+ - table-retrieval
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+ - text
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+ pretty_name: NQTables
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+ config_names:
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+ - default
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+ - queries
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+ - corpus_linearized
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+ - corpus_md
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+ - corpus_structure
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+
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+ dataset_info:
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+ - config_name: default
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+ features:
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+ - name: qid
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+ - name: text
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+ configs:
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+ - config_name: default
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+ data_files:
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+ - split: train
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+ path: train_qrels.jsonl
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+ - split: dev
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+ path: dev_qrels.jsonl
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+ - split: test
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+ - config_name: queries
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+ - split: train_queries
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+ path: train_queries.jsonl
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+ - split: dev_queries
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+ path: test_queries.jsonl
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+ - config_name: corpus_linearized
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+ - split: corpus_linearized
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+ path: corpus_linearized.jsonl
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+ - config_name: corpus_md
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+ data_files:
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+ - split: corpus_md
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+ path: corpus_md.jsonl
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+ - config_name: corpus_structure
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+ data_files:
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+ - split: corpus_structure
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+ path: corpus_structure.jsonl
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+ ---
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+
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+ # NQTables Retrieval
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+
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+ 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`.
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+
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+ ## Configs
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+
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+ | Config | Description | Split(s) |
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+ |---|---|---|
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+ | `default` | Relevance judgments (qrels): `qid`, `did`, `score` | `train`, `dev`, `test` |
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+ | `queries` | Query IDs and text | `train_queries`, `dev_queries`, `test_queries` |
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+ | `corpus_linearized` | Linearized table representation | `corpus_linearized` |
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+ | `corpus_md` | Markdown table representation | `corpus_md` |
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+ | `corpus_structure` | Structured corpus with `headers`, `cells`, `meta_data`. `text` field corresponds to linearized Text + Table. | `corpus_structure` |
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+
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+ ## `corpus_structure` additional fields
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+
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+ | Field | Type | Description |
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+ |---|---|---|
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+ | `meta_data` | string | Table metadata / caption |
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+ | `headers` | list[string] | Column headers |
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+ | `cells` | list[string] | Flattened cell values |
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+
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+ ## TableIR Benchmark Statistics
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+
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+ | Dataset | Structured | #Train | #Dev | #Test | #Corpus |
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+ |---|:---:|---:|---:|---:|---:|
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+ | OpenWikiTables | ✓ | 53.8k | 6.6k | 6.6k | 24.7k |
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+ | NQTables | ✓ | 9.6k | 1.1k | 1k | 170k |
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+ | FeTaQA | ✓ | 7.3k | 1k | 2k | 10.3k |
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+ | OTT-QA (small) | ✓ | 41.5k | 2.2k | -- | 8.8k |
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+ | MultiHierTT | ✗ | -- | 929 | -- | 9.9k |
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+ | AIT-QA | ✗ | -- | -- | 515 | 1.9k |
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+ | StatcanRetrieval | ✗ | -- | -- | 870 | 5.9k |
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+ | watsonxDocsQA | ✗ | -- | -- | 30 | 1.1k |
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+
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+ ## Citation
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+
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+ If you use **TableIR Eval: Table-Text IR Evaluation Collection**, please cite:
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+
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+ ```bibtex
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+ @misc{doshi2026tableir,
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+ title = {TableIR Eval: Table-Text IR Evaluation Collection},
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+ author = {Doshi, Meet and Boni, Odellia and Kumar, Vishwajeet and Sen, Jaydeep and Joshi, Sachindra},
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+ year = {2026},
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+ institution = {IBM Research},
177
+ howpublished = {https://huggingface.co/collections/ibm-research/table-text-ir-evaluation},
178
+ note = {Hugging Face dataset collection}
179
+ }
180
+ ```
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+
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+ All credit goes to original authors. Please cite their work:
183
+ ```bibtex
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+ @inproceedings{herzig-etal-2021-open,
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+ title = "Open Domain Question Answering over Tables via Dense Retrieval",
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+ author = {Herzig, Jonathan and
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+ M{\"u}ller, Thomas and
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+ Krichene, Syrine and
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+ Eisenschlos, Julian},
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+ editor = "Toutanova, Kristina and
191
+ Rumshisky, Anna and
192
+ Zettlemoyer, Luke and
193
+ Hakkani-Tur, Dilek and
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+ Beltagy, Iz and
195
+ Bethard, Steven and
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+ Cotterell, Ryan and
197
+ Chakraborty, Tanmoy and
198
+ Zhou, Yichao",
199
+ booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
200
+ month = jun,
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+ year = "2021",
202
+ address = "Online",
203
+ publisher = "Association for Computational Linguistics",
204
+ url = "https://aclanthology.org/2021.naacl-main.43/",
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+ doi = "10.18653/v1/2021.naacl-main.43",
206
+ pages = "512--519",
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+ 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."
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+ }
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+ ```
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