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2023 TREC Deep Learning Track Dataset

The Deep Learning track focuses on IR tasks where a large training set is available, allowing us to compare a variety of retrieval approaches including deep neural networks and strong non-neural approaches, to see what works best in a large-data regime.

About this Dataset

Updated: 2024-09-06
Metadata Last Updated: 2024-05-08 00:00:00
Date Created: N/A
Data Provided by:
Dataset Owner: N/A

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Table representation of structured data
Title 2023 TREC Deep Learning Track Dataset
Description The Deep Learning track focuses on IR tasks where a large training set is available, allowing us to compare a variety of retrieval approaches including deep neural networks and strong non-neural approaches, to see what works best in a large-data regime.
Modified 2024-05-08 00:00:00
Publisher Name National Institute of Standards and Technology
Contact mailto:[email protected]
Keywords TREC text retrieval conference
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        "fn": "Ian Soboroff"
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    "title": "2023 TREC Deep Learning Track Dataset",
    "description": "The Deep Learning track focuses on IR tasks where a large training set is available, allowing us to compare a variety of retrieval approaches including deep neural networks and strong non-neural approaches, to see what works best in a large-data regime.",
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            "title": "2023 Deep Learning Data Page"
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            "accessURL": "https:\/\/microsoft.github.io\/msmarco\/TREC-Deep-Learning#passage-ranking-dataset",
            "title": "Passage Ranking Corpus"
        },
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            "accessURL": "https:\/\/microsoft.github.io\/msmarco\/TREC-Deep-Learning#document-ranking-dataset",
            "title": "Document Ranking Corpus"
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            "accessURL": "https:\/\/microsoft.github.io\/msmarco\/TREC-Deep-Learning#passage-ranking-dataset",
            "title": "Passage Ranking Topics"
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            "title": "Passage Ranking QRels"
        },
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            "title": "Document Ranking QRels"
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            "title": "Passage Ranking NIST QRels"
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            "title": "Document Ranking NIST QRels"
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