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
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 |
{ "identifier": "ark:\/88434\/mds2-3259", "accessLevel": "public", "contactPoint": { "hasEmail": "mailto:[email protected]", "fn": "Ian Soboroff" }, "programCode": [ "006:045" ], "landingPage": "https:\/\/data.nist.gov\/od\/id\/mds2-3259", "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.", "language": [ "en" ], "distribution": [ { "accessURL": "https:\/\/trec.nist.gov\/data\/deep2023.html", "title": "2023 Deep Learning Data Page" }, { "accessURL": "https:\/\/microsoft.github.io\/msmarco\/TREC-Deep-Learning#passage-ranking-dataset", "title": "Passage Ranking Corpus" }, { "accessURL": "https:\/\/microsoft.github.io\/msmarco\/TREC-Deep-Learning#document-ranking-dataset", "title": "Document Ranking Corpus" }, { "accessURL": "https:\/\/microsoft.github.io\/msmarco\/TREC-Deep-Learning#passage-ranking-dataset", "title": "Passage Ranking Topics" }, { "accessURL": "https:\/\/microsoft.github.io\/msmarco\/TREC-Deep-Learning#passage-ranking-dataset", "title": "Passage Ranking QRels" }, { "accessURL": "https:\/\/microsoft.github.io\/msmarco\/TREC-Deep-Learning#document-ranking-dataset", "title": "Document Ranking QRels" }, { "accessURL": "https:\/\/trec.nist.gov\/data\/deep\/2023.qrels.pass.withDupes.txt", "title": "Passage Ranking NIST QRels" }, { "accessURL": "https:\/\/trec.nist.gov\/data\/deep\/2023.qrels.pass.withDupes.no1.txt", "title": "Passage Ranking (NIST, 1 judgments mapped to 0)" }, { "accessURL": "https:\/\/trec.nist.gov\/data\/deep\/2023.qrels.docs.wihDupes.txt", "title": "Document Ranking NIST QRels" }, { "accessURL": "https:\/\/trec.nist.gov\/data\/deep\/2023.qrels.docs.wihDupes.no1.txt", "title": "Document Ranking (NIST, 1 judgments mapped to 0)" } ], "bureauCode": [ "006:55" ], "modified": "2024-05-08 00:00:00", "publisher": { "@type": "org:Organization", "name": "National Institute of Standards and Technology" }, "theme": [ "Information Technology:Data and informatics" ], "keyword": [ "TREC text retrieval conference" ] }