Datasets:
Tasks:
Text Retrieval
Modalities:
Text
Formats:
json
Sub-tasks:
document-retrieval
Languages:
English
Size:
100K - 1M
License:
| 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." | |
| } | |
| ``` |