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README.md

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----
-license: apache-2.0
----
+---
+annotations_creators:
+  - derived
+language:
+  - eng
+license: apache-2.0
+multilinguality: monolingual
+task_categories:
+  - text-retrieval
+task_ids:
+  - document-retrieval
+tags:
+  - table-retrieval
+  - text
+pretty_name: WatsonxDocsQA
+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: test
+        num_bytes: 2451
+        num_examples: 30
+  - config_name: queries
+    features:
+      - name: _id
+        dtype: string
+      - name: text
+        dtype: string
+    splits:
+      - name: test_queries
+        num_bytes: 2507
+        num_examples: 30
+  - config_name: corpus
+    features:
+      - name: _id
+        dtype: string
+      - name: title
+        dtype: string
+      - name: text
+        dtype: string
+    splits:
+      - name: corpus
+        num_bytes: 5843643
+        num_examples: 1144
+
+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
+    data_files:
+      - split: corpus
+        path: corpus.jsonl
+---
+
+# WatsonxDocsQA Retrieval
+
+This dataset is part of a Table + Text retrieval benchmark. Includes queries and relevance judgments across test split(s), with corpus in 1 format(s): `corpus`.
+
+## Configs
+
+| Config | Description | Split(s) |
+|---|---|---|
+| `default` | Relevance judgments (qrels): `qid`, `did`, `score` | `test` |
+| `queries` | Query IDs and text | `test_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}
+}
+```
+
+This repository is just a reformatted version of [ibm-research/watsonxDocsQA](https://huggingface.co/datasets/ibm-research/watsonxDocsQA).
+
+All credit goes to original authors. Please cite their work:
+```bibtex
+@misc{orbach2025analysishyperparameteroptimizationmethods,
+      title={An Analysis of Hyper-Parameter Optimization Methods for Retrieval Augmented Generation}, 
+      author={Matan Orbach and Ohad Eytan and Benjamin Sznajder and Ariel Gera and Odellia Boni and Yoav Kantor and Gal Bloch and Omri Levy and Hadas Abraham and Nitzan Barzilay and Eyal Shnarch and Michael E. Factor and Shila Ofek-Koifman and Paula Ta-Shma and Assaf Toledo},
+      year={2025},
+      eprint={2505.03452},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2505.03452}, 
+}
+```

test_qrels.jsonl

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+{"qid": "test_1", "did": "5B37710FE7BBD6EFB842FEB7B49B036302E18F81", "score": 1}
+{"qid": "test_2", "did": "42AE491240EF740E6A8C5CF32B817E606F554E49", "score": 1}
+{"qid": "test_3", "did": "51747F17F413F1F34CFD73D170DE392D874D03DD", "score": 1}
+{"qid": "test_4", "did": "B193A2795BDEF17A5D204CDD18188A767E2FE7B7", "score": 1}
+{"qid": "test_5", "did": "F003581774D3028EF53E61A002C20A6D36BA8E00", "score": 1}
+{"qid": "test_6", "did": "6049D5AA5DE41309E6281534A464ABD6898A758C", "score": 1}
+{"qid": "test_7", "did": "78A8C07B83DF1B01276353D098E84F12304636E2", "score": 1}
+{"qid": "test_8", "did": "42AE491240EF740E6A8C5CF32B817E606F554E49", "score": 1}
+{"qid": "test_9", "did": "38FB0908B90954D96CEFF54BA975DE832286A0A7", "score": 1}
+{"qid": "test_10", "did": "B2117B2CD0FEA469149B23FACB6A9F7F32905AFD", "score": 1}
+{"qid": "test_11", "did": "F003581774D3028EF53E61A002C20A6D36BA8E00", "score": 1}
+{"qid": "test_12", "did": "717B697E0045B5D7DFF6ACC93AD5DEC98E27EBDC", "score": 1}
+{"qid": "test_13", "did": "F003581774D3028EF53E61A002C20A6D36BA8E00", "score": 1}
+{"qid": "test_14", "did": "43785386700CF73E37A8F76ADC4EF9FB01EE0AEB", "score": 1}
+{"qid": "test_15", "did": "4B48EF3D089F3142B1ED604A32873217F89E052F", "score": 1}
+{"qid": "test_16", "did": "A7845D8C3E419CEDD06E8C447ADF41E6E3D860C8", "score": 1}
+{"qid": "test_17", "did": "315971AE6C6A4EEDE13E9E1449B2A36F548B928F", "score": 1}
+{"qid": "test_18", "did": "F003581774D3028EF53E61A002C20A6D36BA8E00", "score": 1}
+{"qid": "test_19", "did": "3508F0DDA4CCBDBB07BD583218F4E4260DC01C0D", "score": 1}
+{"qid": "test_20", "did": "F5AF4BCC2D0168D2698BEB2A858C24F81A476610", "score": 1}
+{"qid": "test_21", "did": "BE7F45C3E17998A50B8414D623007ED668B37C04", "score": 1}
+{"qid": "test_22", "did": "82546B72EDBFB76F571CFD06A7009E01615FA054", "score": 1}
+{"qid": "test_23", "did": "F003581774D3028EF53E61A002C20A6D36BA8E00", "score": 1}
+{"qid": "test_24", "did": "977C81385F7825613F1EDBD3C0DBF44C259BA8D7", "score": 1}
+{"qid": "test_25", "did": "B481BDE61EEBA2CC6B2A0C1D9C43D8DD56AB2A08", "score": 1}
+{"qid": "test_26", "did": "FE2254205E6DD1EE2A4EC62036AB86BC5E084F5D", "score": 1}
+{"qid": "test_27", "did": "15D57C8193B99B8525BC2999EF82EF1CD7EAE8AD", "score": 1}
+{"qid": "test_28", "did": "ABFAAF84948B090C8EA099FF44CC8CD878371073", "score": 1}
+{"qid": "test_29", "did": "F003581774D3028EF53E61A002C20A6D36BA8E00", "score": 1}
+{"qid": "test_30", "did": "7946DCF2F69A7420490A7B5CA677C2273DE5764B", "score": 1}

test_qrels.tsv

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+qid	did	score
+test_1	5B37710FE7BBD6EFB842FEB7B49B036302E18F81	1
+test_2	42AE491240EF740E6A8C5CF32B817E606F554E49	1
+test_3	51747F17F413F1F34CFD73D170DE392D874D03DD	1
+test_4	B193A2795BDEF17A5D204CDD18188A767E2FE7B7	1
+test_5	F003581774D3028EF53E61A002C20A6D36BA8E00	1
+test_6	6049D5AA5DE41309E6281534A464ABD6898A758C	1
+test_7	78A8C07B83DF1B01276353D098E84F12304636E2	1
+test_8	42AE491240EF740E6A8C5CF32B817E606F554E49	1
+test_9	38FB0908B90954D96CEFF54BA975DE832286A0A7	1
+test_10	B2117B2CD0FEA469149B23FACB6A9F7F32905AFD	1
+test_11	F003581774D3028EF53E61A002C20A6D36BA8E00	1
+test_12	717B697E0045B5D7DFF6ACC93AD5DEC98E27EBDC	1
+test_13	F003581774D3028EF53E61A002C20A6D36BA8E00	1
+test_14	43785386700CF73E37A8F76ADC4EF9FB01EE0AEB	1
+test_15	4B48EF3D089F3142B1ED604A32873217F89E052F	1
+test_16	A7845D8C3E419CEDD06E8C447ADF41E6E3D860C8	1
+test_17	315971AE6C6A4EEDE13E9E1449B2A36F548B928F	1
+test_18	F003581774D3028EF53E61A002C20A6D36BA8E00	1
+test_19	3508F0DDA4CCBDBB07BD583218F4E4260DC01C0D	1
+test_20	F5AF4BCC2D0168D2698BEB2A858C24F81A476610	1
+test_21	BE7F45C3E17998A50B8414D623007ED668B37C04	1
+test_22	82546B72EDBFB76F571CFD06A7009E01615FA054	1
+test_23	F003581774D3028EF53E61A002C20A6D36BA8E00	1
+test_24	977C81385F7825613F1EDBD3C0DBF44C259BA8D7	1
+test_25	B481BDE61EEBA2CC6B2A0C1D9C43D8DD56AB2A08	1
+test_26	FE2254205E6DD1EE2A4EC62036AB86BC5E084F5D	1
+test_27	15D57C8193B99B8525BC2999EF82EF1CD7EAE8AD	1
+test_28	ABFAAF84948B090C8EA099FF44CC8CD878371073	1
+test_29	F003581774D3028EF53E61A002C20A6D36BA8E00	1
+test_30	7946DCF2F69A7420490A7B5CA677C2273DE5764B	1

test_queries.jsonl

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+{"_id": "test_1", "text": "What foundation models are available in watsonx.ai ?"}
+{"_id": "test_2", "text": "What is greedy decoding?"}
+{"_id": "test_3", "text": "What tuning parameters are available for IBM foundation models?"}
+{"_id": "test_4", "text": "What are tokens and tokenization?"}
+{"_id": "test_5", "text": "What is the \"random seed\" parameter in prompt tuning experiments?"}
+{"_id": "test_6", "text": "How to build reusable prompts?"}
+{"_id": "test_7", "text": "What are the functionalities of the Prompt Lab in IBM watsonx.ai, and how does it facilitate the process of crafting and optimizing prompts for deployed foundation models?"}
+{"_id": "test_8", "text": "How do I avoid repetitive text in prompt tuning experiments?"}
+{"_id": "test_9", "text": "What happens to unsaved prompt text within Prompt Lab, and how long does it persist on the webpage before being deleted?"}
+{"_id": "test_10", "text": "Why deploy a prompt template?"}
+{"_id": "test_11", "text": "What is trust calibration?"}
+{"_id": "test_12", "text": "What are parameters in CLEM?"}
+{"_id": "test_13", "text": "What is a data warehouse?"}
+{"_id": "test_14", "text": "What is the retrieval-augmented generation pattern?"}
+{"_id": "test_15", "text": "What is the workflow for a Federated Learning experiment?"}
+{"_id": "test_16", "text": "What are the benefits of using IBM Federated Learning?"}
+{"_id": "test_17", "text": "How do I delete a deployment using the Python client?"}
+{"_id": "test_18", "text": "What is an ontology?"}
+{"_id": "test_19", "text": "What are the key functions of the geospatial library?"}
+{"_id": "test_20", "text": "What is a map chart?"}
+{"_id": "test_21", "text": "What versions of IBM Db2 for z/OS are supported?"}
+{"_id": "test_22", "text": "What is the purpose of the Sim Eval node in IBM SPSS Modeler?"}
+{"_id": "test_23", "text": "What is a deterministic computing system?"}
+{"_id": "test_24", "text": "How can I search for assets across the platform?"}
+{"_id": "test_25", "text": "What file types are supported by the Box connection?"}
+{"_id": "test_26", "text": "What are the properties of the Bayes Net node in the Clementine data mining tool?"}
+{"_id": "test_27", "text": "What are the natural language processing tasks supported in the Watson Natural Language Processing library?"}
+{"_id": "test_28", "text": "What is an IBM Cloud service ID?"}
+{"_id": "test_29", "text": "What is sentiment analysis?"}
+{"_id": "test_30", "text": "How do I create a connection to Microsoft SQL Server?"}