dataset_infos.json

{
  "queries": {
    "description": "FutureQueryEval query collection with 148 queries across 7 categories",
    "citation": "@misc{abdallah2025good, title={How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models}, author={Abdelrahman Abdallah and Bhawna Piryani and Jamshid Mozafari and Mohammed Ali and Adam Jatowt}, year={2025}, eprint={2508.16757}, archivePrefix={arXiv}, primaryClass={cs.CL}}",
    "homepage": "https://github.com/DataScienceUIBK/llm-reranking-generalization-study",
    "license": "Apache-2.0",
    "features": {
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        "dtype": "string",
        "id": null,
        "_type": "Value"
      },
      "query_text": {
        "dtype": "string", 
        "id": null,
        "_type": "Value"
      },
      "category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
      }
    },
    "splits": {
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        "name": "queries",
        "num_bytes": 45056,
        "num_examples": 148,
        "dataset_name": "future_query_eval"
      }
    },
    "download_size": 45056,
    "dataset_size": 45056
  },
  "corpus": {
    "description": "FutureQueryEval document corpus with 2,787 documents",
    "citation": "@misc{abdallah2025good, title={How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models}, author={Abdelrahman Abdallah and Bhawna Piryani and Jamshid Mozafari and Mohammed Ali and Adam Jatowt}, year={2025}, eprint={2508.16757}, archivePrefix={arXiv}, primaryClass={cs.CL}}",
    "homepage": "https://github.com/DataScienceUIBK/llm-reranking-generalization-study",
    "license": "Apache-2.0",
    "features": {
      "doc_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
      },
      "title": {
        "dtype": "string",
        "id": null, 
        "_type": "Value"
      },
      "text": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
      },
      "url": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
      }
    },
    "splits": {
      "corpus": {
        "name": "corpus",
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        "num_examples": 2787,
        "dataset_name": "future_query_eval"
      }
    },
    "download_size": 964608,
    "dataset_size": 964608
  },
  "qrels": {
    "description": "FutureQueryEval relevance judgments with 2,938 query-document pairs",
    "citation": "@misc{abdallah2025good, title={How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models}, author={Abdelrahman Abdallah and Bhawna Piryani and Jamshid Mozafari and Mohammed Ali and Adam Jatowt}, year={2025}, eprint={2508.16757}, archivePrefix={arXiv}, primaryClass={cs.CL}}",
    "homepage": "https://github.com/DataScienceUIBK/llm-reranking-generalization-study", 
    "license": "Apache-2.0",
    "features": {
      "query_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
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      "iteration": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
      },
      "doc_id": {
        "dtype": "string", 
        "id": null,
        "_type": "Value"
      },
      "relevance": {
        "dtype": "int32",
        "id": null,
        "_type": "Value"
      }
    },
    "splits": {
      "qrels": {
        "name": "qrels",
        "num_bytes": 99328,
        "num_examples": 2938,
        "dataset_name": "future_query_eval"
      }
    },
    "download_size": 99328,
    "dataset_size": 99328
  }
}