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  "access_rights": "",
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  "access_rights": "",
            
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  "author": "Tri Nguyen",
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  "author": "Tri Nguyen",
            
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  "author_email": "",
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  "author_email": "",
            
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  "citation": [
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  "citation": [
            
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    "https://doi.org/10.48550/arXiv.2004.12832",
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    "https://doi.org/10.48550/arXiv.2004.12832",
            
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    "https://doi.org/10.48550/arXiv.1911.03407"
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    "https://doi.org/10.48550/arXiv.1911.03407"
            
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  ],
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  "defined_in": "https://doi.org/10.48550/arXiv.1904.07531",
            
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  "doi": "10.57702/y9ev8opk",
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  "doi": "10.57702/y9ev8opk",
            
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  "doi_date_published": "2024-11-25",
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  "doi_date_published": "2024-11-25",
            
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  "doi_publisher": "TIB",
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  "doi_publisher": "TIB",
            
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  "doi_status": true,
            
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  "domain": "https://service.tib.eu/ldmservice",
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  "extra_authors": [
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  "extra_authors": [
            
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    {
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    {
            
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      "extra_author": "Mir Rosenberg",
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      "extra_author": "Mir Rosenberg",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Xia Song",
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      "extra_author": "Xia Song",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Jianfeng Gao",
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      "extra_author": "Jianfeng Gao",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Saurabh Tiwary",
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      "extra_author": "Saurabh Tiwary",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Rangan Majumder",
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      "extra_author": "Rangan Majumder",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Li Deng",
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      "extra_author": "Li Deng",
            
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      "orcid": ""
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      "orcid": ""
            
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    }
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    }
            
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  ],
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  "landing_page": "https://microsoft.github.io/msmarco/",
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  "metadata_created": "2024-11-25T14:23:55.046003",
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  "metadata_created": "2024-11-25T14:23:55.046003",
            
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  "metadata_modified": "2024-11-25T14:37:34.345765",
            
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  "name": "ms-marco",
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  "name": "ms-marco",
            
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  "notes": "MS MARCO is a dataset designed for reading comprehension 
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  "notes": "MS MARCO is a dataset designed for reading comprehension 
            
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and retrieval tasks, containing 8.8 million passages collected from 
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and retrieval tasks, containing 8.8 million passages collected from 
            
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Bing's results to 1 million queries. It is used to evaluate the 
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Bing's results to 1 million queries. It is used to evaluate the 
            
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effectiveness of neural ranking models.",
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effectiveness of neural ranking models.",
            
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    "approval_status": "approved",
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    "approval_status": "approved",
            
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    "created": "2024-11-25T12:11:38.292601",
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      "id": "d981bbd2-27ef-4211-bf7a-95f19e6078a9",

            
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      "last_modified": "2024-11-25T14:23:55.403325",

            
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  "tags": [],
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  "tags": [],
            
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  "title": "MS MARCO",
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  "title": "MS MARCO",
            
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  "version": "v1.1"
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  "version": "v1.1"
            
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}