asd

dataset_infos.json

@@ -1,111 +0,0 @@
-{
-  "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": {
-      "query_id": {
-        "dtype": "string",
-        "id": null,
-        "_type": "Value"
-      },
-      "query_text": {
-        "dtype": "string", 
-        "id": null,
-        "_type": "Value"
-      },
-      "category": {
-        "dtype": "string",
-        "id": null,
-        "_type": "Value"
-      }
-    },
-    "splits": {
-      "queries": {
-        "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",
-        "num_bytes": 964608,
-        "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"
-      },
-      "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
-  }
-}