├── .gitignore ├── CODE_OF_CONDUCT.md ├── LICENSE ├── LICENSE-CCA ├── README.md ├── SECURITY.md └── SUPPORT.md /.gitignore: -------------------------------------------------------------------------------- 1 | ## Ignore Visual Studio temporary files, build results, and 2 | ## files generated by popular Visual Studio add-ons. 3 | ## 4 | ## Get latest from https://github.com/github/gitignore/blob/main/VisualStudio.gitignore 5 | 6 | # User-specific files 7 | *.rsuser 8 | *.suo 9 | *.user 10 | *.userosscache 11 | *.sln.docstates 12 | 13 | # User-specific files (MonoDevelop/Xamarin Studio) 14 | *.userprefs 15 | 16 | # Mono auto generated files 17 | mono_crash.* 18 | 19 | # Build results 20 | [Dd]ebug/ 21 | [Dd]ebugPublic/ 22 | [Rr]elease/ 23 | [Rr]eleases/ 24 | x64/ 25 | x86/ 26 | [Ww][Ii][Nn]32/ 27 | [Aa][Rr][Mm]/ 28 | [Aa][Rr][Mm]64/ 29 | bld/ 30 | [Bb]in/ 31 | [Oo]bj/ 32 | [Ll]og/ 33 | [Ll]ogs/ 34 | 35 | # Visual Studio 2015/2017 cache/options directory 36 | .vs/ 37 | # Uncomment if you have tasks that create the project's static files in wwwroot 38 | #wwwroot/ 39 | 40 | # Visual Studio 2017 auto generated files 41 | Generated\ Files/ 42 | 43 | # MSTest test Results 44 | [Tt]est[Rr]esult*/ 45 | [Bb]uild[Ll]og.* 46 | 47 | # NUnit 48 | *.VisualState.xml 49 | TestResult.xml 50 | nunit-*.xml 51 | 52 | # Build Results of an ATL Project 53 | [Dd]ebugPS/ 54 | [Rr]eleasePS/ 55 | dlldata.c 56 | 57 | # Benchmark Results 58 | BenchmarkDotNet.Artifacts/ 59 | 60 | # .NET Core 61 | project.lock.json 62 | project.fragment.lock.json 63 | artifacts/ 64 | 65 | # ASP.NET Scaffolding 66 | ScaffoldingReadMe.txt 67 | 68 | # StyleCop 69 | StyleCopReport.xml 70 | 71 | # Files built by Visual Studio 72 | *_i.c 73 | *_p.c 74 | *_h.h 75 | *.ilk 76 | *.meta 77 | *.obj 78 | *.iobj 79 | *.pch 80 | *.pdb 81 | *.ipdb 82 | *.pgc 83 | *.pgd 84 | *.rsp 85 | *.sbr 86 | *.tlb 87 | *.tli 88 | *.tlh 89 | *.tmp 90 | *.tmp_proj 91 | *_wpftmp.csproj 92 | *.log 93 | *.tlog 94 | *.vspscc 95 | *.vssscc 96 | .builds 97 | *.pidb 98 | *.svclog 99 | *.scc 100 | 101 | # Chutzpah Test files 102 | _Chutzpah* 103 | 104 | # Visual C++ cache files 105 | ipch/ 106 | *.aps 107 | *.ncb 108 | *.opendb 109 | *.opensdf 110 | *.sdf 111 | *.cachefile 112 | *.VC.db 113 | *.VC.VC.opendb 114 | 115 | # Visual Studio profiler 116 | *.psess 117 | *.vsp 118 | *.vspx 119 | *.sap 120 | 121 | # Visual Studio Trace Files 122 | *.e2e 123 | 124 | # TFS 2012 Local Workspace 125 | $tf/ 126 | 127 | # Guidance Automation Toolkit 128 | *.gpState 129 | 130 | # ReSharper is a .NET coding add-in 131 | _ReSharper*/ 132 | *.[Rr]e[Ss]harper 133 | *.DotSettings.user 134 | 135 | # TeamCity is a build add-in 136 | _TeamCity* 137 | 138 | # DotCover is a Code Coverage Tool 139 | *.dotCover 140 | 141 | # AxoCover is a Code Coverage Tool 142 | .axoCover/* 143 | !.axoCover/settings.json 144 | 145 | # Coverlet is a free, cross platform Code Coverage Tool 146 | coverage*.json 147 | coverage*.xml 148 | coverage*.info 149 | 150 | # Visual Studio code coverage results 151 | *.coverage 152 | *.coveragexml 153 | 154 | # NCrunch 155 | _NCrunch_* 156 | .*crunch*.local.xml 157 | nCrunchTemp_* 158 | 159 | # MightyMoose 160 | *.mm.* 161 | AutoTest.Net/ 162 | 163 | # Web workbench (sass) 164 | .sass-cache/ 165 | 166 | # Installshield output folder 167 | [Ee]xpress/ 168 | 169 | # DocProject is a documentation generator add-in 170 | DocProject/buildhelp/ 171 | DocProject/Help/*.HxT 172 | DocProject/Help/*.HxC 173 | DocProject/Help/*.hhc 174 | DocProject/Help/*.hhk 175 | DocProject/Help/*.hhp 176 | DocProject/Help/Html2 177 | DocProject/Help/html 178 | 179 | # Click-Once directory 180 | publish/ 181 | 182 | # Publish Web Output 183 | *.[Pp]ublish.xml 184 | *.azurePubxml 185 | # Note: Comment the next line if you want to checkin your web deploy settings, 186 | # but database connection strings (with potential passwords) will be unencrypted 187 | *.pubxml 188 | *.publishproj 189 | 190 | # Microsoft Azure Web App publish settings. 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For 392 | the avoidance of doubt, this paragraph does not form part of the 393 | public licenses. 394 | 395 | Creative Commons may be contacted at creativecommons.org. 396 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Datasets for MS MARCO Web Search 2 | 3 | ## Introduction 4 | 5 | MS MARCO Web Search is a large-scale information-rich Web dataset, featuring millions of real clicked query-document labels. This dataset closely mimics real-world web document and 6 | query distribution, provides rich information for various kinds of downstream tasks. It incorporates the largest open web document dataset, ClueWeb22, as our document corpus. 7 | ClueWeb22 includes about 10 billion high-quality web pages, sufficiently large to serve as representative web-scale data. It also contains rich information from the web pages, such as visual 8 | representation rendered by web browsers, raw HTML structure, clean text, semantic annotations, language and topic tags labeled by industry document understanding systems, etc. MS MARCO Web 9 | Search further contains 10 million unique queries from 93 languages with millions of relevant labeled query-document pairs collected from the search log of the Microsoft Bing search engine to serve 10 | as the query set. 11 | 12 | It offers a retrieval benchmark on 100 million document set with three web retrieval challenge tasks that demands innovations in both machine learning and information retrieval 13 | system research domains: embedding model, embedding retrieval, and end-to-end retrieval challenges. The main goal of the leaderboard is to study what retrieval methods work best and what retrieval 14 | methods are cost-efficient when a large amount of data is available. 15 | 16 | Moreover, MS MARCO Web Search also offers 5 times larger real click labels for the whole 10 billion document set. Researchers can use this dataset to verify whether methods that work on small data also work on large data. 17 | 18 | ## Citation 19 | 20 | If you use the MS MARCO Web Search dataset, or any dataset derived from it, please cite the [paper](https://arxiv.org/abs/2405.07526): 21 | @inproceedings{MSMARCOWEBSEARCH_2024, 22 | series={WWW ’24}, 23 | title={MS MARCO Web Search: A Large-scale Information-rich Web Dataset with Millions of Real Click Labels}, 24 | booktitle={Companion Proceedings of the ACM on Web Conference 2024}, 25 | author={Chen, Qi and Geng, Xiubo and Rosset, Corby and Buractaon, Carolyn and Lu, Jingwen and Shen, Tao and Zhou, Kun and Xiong, Chenyan and Gong, Yeyun and Bennett, Paul and Craswell, Nick and Xie, Xing and Yang, Fan and Tower, Bryan and Rao, Nikhil and Dong, Anlei and Jiang, Wenqi and Liu, Zheng and Li, Mingqin and Liu, Chuanjie and Li, Zengzhong and Majumder, Rangan and Neville, Jennifer and Oakley, Andy and Risvik, Knut Magne and Simhadri, Harsha Vardhan and Varma, Manik and Wang, Yujing and Yang, Linjun and Yang, Mao and Zhang, Ce}, 26 | year={2024}, 27 | } 28 | ## Tasks 29 | 30 | There are three tasks: embedding model, embedding retrieval, and end-to-end retrieval rankings. 31 | 32 | ### Embedding Model Ranking Task 33 | 34 | The first task focuses on embedding model ranking. The large-scale web data volume requires large embedding models to guarantee sufficient knowledge coverage. It requires balancing the following two goals: good model generalization ability 35 | and efficient train/inference speed. Given a query, you are expected to rank documents from the full collection based on their relevance to the query. You can submit up to **100 documents** for this task. It models the embedding model quality. The metrics we evaluate include: 36 | - Mean Reciprocal Rank (MRR): the average of the multiplicative inverse of the rank of the first correct result, which is widely used for evaluating the model quality. 37 | - Recall: the average percentage of ground truth items (test query-document labels) recalled during the search. 38 | - Throughput (QPS): All queries are provided at once, and we measure the wall clock time between the ingestion of the vectors and when all the results are output using all the threads in a machine. Then the throughput is calculated as the processed queries per second (QPS). 39 | - Latency: we measure the 50, 90 and 99 percentile query latency at certain QPS. 40 | 41 | |Baselines | MRR@10 | recall@1 | recall@5 | recall@10 | recall@20 | recall@100 | QPS | P50 latency | P90 latency | P99 latency | 42 | | --- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | 43 | |[DPR](https://github.com/facebookresearch/DPR) | 0.542 | 45.12% | 66.04% | 72.10% | 76.80% | 87.54% | 698 | 9.896 ms | 10.018 ms | 11.430 ms | 44 | |[ANCE](https://github.com/microsoft/ANCE) |0.633 | 54.18% | 75.53% | 80.53% | 84.17% | 91.17% | 698 | 9.896 ms | 10.018 ms | 11.430 ms | 45 | |[SimANS](https://github.com/microsoft/SimXNS/tree/main/SimANS) |0.649 | 55.86% | 76.84% | 81.78% | 85.23% | 91.98% | 698 | 9.896 ms | 10.018 ms | 11.430 ms | 46 | 47 | ### Embedding Retrieval Ranking Task 48 | 49 | Embedding models need to co-work with the embedding retrieval system to serve a web scale dataset. The second task focuses on embedding retrieval algorithm/system performance and accuracy. We take the embedding vectors 50 | generated by one of the baseline models as the ANN vector set. The goal of this challenge is to call for ANN algorithm innovations to minimize the accuracy gap between approximate search and 51 | brute-force search while still preserving good system performance. In this task, we only evaluate ANN Recall (take the brute-force vector search results as the ground truth), Throughout and Latency. 52 | 53 | |Baselines | ANN recall@1 | ANN recall@10 | ANN recall@100 | QPS | P50 latency | P90 latency | P99 latency | 54 | | --- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | 55 | |[SPANN](https://github.com/microsoft/SPTAG) | 87.97% | 80.55% | 69.84% | 625 | 10.411 ms | 10.873 ms | 11.334 ms | 56 | |[DiskANN](https://github.com/microsoft/DiskANN) | 91.46% | 87.07% | 69.73% | 2691 | 21.968 ms | 37.841 | 69.462 ms | 57 | 58 | ### End-to-end Retrieval Ranking Task 59 | 60 | In the web scenario, the result quality and system performance of the end-to-end retrieval system are the most important metrics in comparing different solutions. This challenge task encourages any kind of solutions, 61 | including an embedding model plus ANN system, inverted index solution, hybrid solution, neural indexer, and large language model, etc. 62 | 63 | |Baselines | MRR@10 | recall@1 | recall@5 | recall@10 | recall@20 | recall@100 | QPS | P50 latency | P90 latency | P99 latency | 64 | | --- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | 65 | |[Elasticsearch BM25](https://github.com/elastic/elasticsearch) | 0.296 | 22.30% | 39.04% | 46.00% | 52.42% | 63.87% | 149 | 312.025 ms | 1065.141 ms | 3745.546 ms | 66 | |[DPR](https://github.com/facebookresearch/DPR) + [SPANN](https://github.com/microsoft/SPTAG) | 0.467 | 39.21% | 56.66% | 61.27% | 64.69% | 70.28% | 625 | 21.924 ms | 23.017 ms | 34.217 ms | 67 | |[ANCE](https://github.com/microsoft/ANCE)+ [SPANN](https://github.com/microsoft/SPTAG) | 0.580 | 49.87% | 68.59% | 72.94% | 75.86% | 80.18% | 625 | 21.924 ms | 23.017 ms | 34.217 ms | 68 | |[SimANS](https://github.com/microsoft/SimXNS/tree/main/SimANS) + [SPANN](https://github.com/microsoft/SPTAG) | 0.585 | 50.63% | 68.79% | 73.14% | 75.85% | 79.82% |625 | 21.924 ms | 23.017 ms | 34.217 ms | 69 | 70 | ## Datasets 71 |
72 | I confirm that I accept the terms and the licenses. Click to see the download links of datasets 73 | 74 | ### 100M dataset 75 | 76 | | Type | Filename | File size | Num Records | Format | 77 | |--------|-----------------------------------------------------------------------------------------------------------------------|----------:|-------------------------:|----------------------------------------------------------------| 78 | | ClueWeb22 Collection | https://lemurproject.org/clueweb22.php/ | --- | 10B | --- | 79 | | Document ID in ClueWeb22 | [doc_hash_mapping.tsv](https://msmarco.z22.web.core.windows.net/msmarcowebsearch/100M_queries/doc_hash_mapping.tsv) | 8.34 GB | 210,894,832 | tsv: docid in ClueWeb22, docid | 80 | | Train | [queries_train.tsv](https://msmarco.z22.web.core.windows.net/msmarcowebsearch/100M_queries/queries_train.tsv) | 678.36 MB | 9,206,475 | tsv: qid, query, languages | 81 | | Train | [qrels_train.tsv](https://msmarco.z22.web.core.windows.net/msmarcowebsearch/100M_queries/qrels_train.tsv) | 194.93 MB | 9,346,695 | TREC qrels format | 82 | | Dev | [queries_dev.tsv](https://msmarco.z22.web.core.windows.net/msmarcowebsearch/100M_queries/queries_dev.tsv) | 675.2 KB | 9,253 | tsv: qid, query, languages | 83 | | Dev | [qrels_dev.tsv](https://msmarco.z22.web.core.windows.net/msmarcowebsearch/100M_queries/qrels_dev.tsv) | 173.19 KB | 9,402 | TREC qrels format | 84 | | Test | [queries_test.tsv](https://msmarco.z22.web.core.windows.net/msmarcowebsearch/100M_queries/queries_test.tsv) | 734.33 KB | 9,374 | tsv: qid, query, languages | 85 | | Test | [qrels_test.tsv](https://msmarco.z22.web.core.windows.net/msmarcowebsearch/100M_queries/qrels_test.tsv) | 180.32 KB | 9,374 | TREC qrels format | 86 | | Document Embedding Vectors | [vectors.bin](https://msmarco.z22.web.core.windows.net/msmarcowebsearch/vectors/SimANS/passage_vectors/vectors.bin), [metaidx.bin](https://msmarco.z22.web.core.windows.net/msmarcowebsearch/vectors/SimANS/passage_vectors/metaidx.bin), [meta.bin](https://msmarco.z22.web.core.windows.net/msmarcowebsearch/vectors/SimANS/passage_vectors/meta.bin) | 289.16GB | 100,924,960 | [Binary Format](https://github.com/microsoft/SPTAG/blob/main/docs/GettingStart.md#input-file-format) | 87 | | Query Embedding Vectors | [vectors.bin](https://msmarco.z22.web.core.windows.net/msmarcowebsearch/vectors/SimANS/query_vectors/vectors.bin), [metaidx.bin](https://msmarco.z22.web.core.windows.net/msmarcowebsearch/vectors/SimANS/query_vectors/metaidx.bin), [meta.bin](https://msmarco.z22.web.core.windows.net/msmarcowebsearch/vectors/SimANS/query_vectors/meta.bin) | 27.47 MB | 9,374 | [Binary Format](https://github.com/microsoft/SPTAG/blob/main/docs/GettingStart.md#input-file-format) | 88 | | Embedding Retrieval Truth | [truth.txt](https://msmarco.z22.web.core.windows.net/msmarcowebsearch/vectors/SimANS/truth.txt) | 7.97 MB | 9,374 | [Truth Format](https://github.com/microsoft/SPTAG/blob/main/docs/GettingStart.md#input-file-format) | 89 | 90 | ### 10B dataset 91 | 92 | | Description | Filename | File size | Num Records | Format | 93 | |-------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------|----------:|-----------------------------------:|----------------------------------------------------------------| 94 | | ClueWeb22 Collection | https://lemurproject.org/clueweb22.php/ | --- | 10B | --- | 95 | | Train | [queries_train.tsv](https://msmarco.z22.web.core.windows.net/msmarcowebsearch/100M_queries/queries_train.tsv) | 678.36 MB | 9,206,475 | tsv: qid, query, languages | 96 | | Train | [qrels_train.tsv](https://msmarco.z22.web.core.windows.net/msmarcowebsearch/10B_queries/qrels_train.tsv) | 2.43 GB | 62,302,553 | TREC qrels format | 97 | | Dev | [queries_dev.tsv](https://msmarco.z22.web.core.windows.net/msmarcowebsearch/100M_queries/queries_dev.tsv) | 675.2 KB | 9,253 | tsv: qid, query, languages | 98 | | Dev | [qrels_dev.tsv](https://msmarco.z22.web.core.windows.net/msmarcowebsearch/10B_queries/qrels_dev.tsv) | 2.35 MB | 63,314 | TREC qrels format | 99 | | Test | [queries_test.tsv](https://msmarco.z22.web.core.windows.net/msmarcowebsearch/100M_queries/queries_test.tsv) | 734.33 KB | 9,374 | tsv: qid, query, languages | 100 | | Test | [qrels_test.tsv](https://msmarco.z22.web.core.windows.net/msmarcowebsearch/10B_queries/qrels_test.tsv) | 2.65 MB | 40,511 | TREC qrels format | 101 | 102 | ### Use of external information 103 | 104 | IMPORTANT NOTE: You are allowed to use external information while developing your runs. 105 | However, it is prohibited to use any datasets in your submission except those listed above. 106 | The original MS MARCO Web Search dataset reveals minor details of how the dataset was constructed that would not be available in a real-world search engine; hence, should be avoided. 107 |
108 | 109 | # Notice 110 | 111 | ## Terms and Conditions 112 | 113 | The MS MARCO Web Search are intended for non-commercial research purposes only to promote advancement in the field of artificial intelligence and related areas, and is made available free of charge without extending any license or other intellectual property rights. 114 | The datasets are provided "as is" without warranty and usage of the data has risks since we may not own the underlying rights in the documents. 115 | We are not be liable for any damages related to use of the dataset. 116 | Feedback is voluntarily given and can be used as we see fit. 117 | By using any of the datasets you are automatically agreeing to abide by these terms and conditions. 118 | Upon violation of any of these terms, your rights to use the dataset will end automatically. 119 | 120 | Please contact us at ms-marco-web@microsoft.com if you own any of the documents made available but do not want them in this dataset. 121 | We will remove the data accordingly. 122 | If you have questions about use of the dataset or any research outputs in your products or services, we encourage you to undertake your own independent legal review. 123 | For other questions, please feel free to contact us. 124 | 125 | ## Contributing 126 | 127 | This project welcomes contributions and suggestions. Most contributions require you to agree to a 128 | Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us 129 | the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com. 130 | 131 | When you submit a pull request, a CLA bot will automatically determine whether you need to provide 132 | a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions 133 | provided by the bot. You will only need to do this once across all repos using our CLA. 134 | 135 | This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). 136 | For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or 137 | contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments. 138 | 139 | ## Legal Notices 140 | 141 | Microsoft and any contributors grant you a license to the Microsoft documentation and other content 142 | in this repository under the [Creative Commons Attribution 4.0 International Public License](https://creativecommons.org/licenses/by/4.0/legalcode), 143 | see the [LICENSE-CCA](LICENSE-CCA) file, and grant you a license to any code in the repository under the [MIT License](https://opensource.org/licenses/MIT), see the 144 | [LICENSE](LICENSE) file. 145 | 146 | Microsoft licenses the MS MARCO Web Search Mark "as-is" and makes no express or implied representations or warranties regarding non-infringement. 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