File size: 9,036 Bytes
45d4cbe 86db073 45d4cbe 86db073 45d4cbe 86db073 45d4cbe 86db073 45d4cbe 86db073 45d4cbe 86db073 45d4cbe 86db073 45d4cbe 86db073 45d4cbe 86db073 45d4cbe |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 |
{
"v1.1": {
"description": "\nStarting with a paper released at NIPS 2016, MS MARCO is a collection of datasets focused on deep learning in search.\n\nThe first dataset was a question answering dataset featuring 100,000 real Bing questions and a human generated answer.\nSince then we released a 1,000,000 question dataset, a natural langauge generation dataset, a passage ranking dataset,\nkeyphrase extraction dataset, crawling dataset, and a conversational search.\n\nThere have been 277 submissions. 20 KeyPhrase Extraction submissions, 87 passage ranking submissions, 0 document ranking\nsubmissions, 73 QnA V2 submissions, 82 NLGEN submisions, and 15 QnA V1 submissions\n\nThis data comes in three tasks/forms: Original QnA dataset(v1.1), Question Answering(v2.1), Natural Language Generation(v2.1).\n\nThe original question answering datset featured 100,000 examples and was released in 2016. Leaderboard is now closed but data is availible below.\n\nThe current competitive tasks are Question Answering and Natural Language Generation. Question Answering features over 1,000,000 queries and\nis much like the original QnA dataset but bigger and with higher quality. The Natural Language Generation dataset features 180,000 examples and\nbuilds upon the QnA dataset to deliver answers that could be spoken by a smart speaker.\n\n\nversion v1.1",
"citation": "\n@article{DBLP:journals/corr/NguyenRSGTMD16,\n author = {Tri Nguyen and\n Mir Rosenberg and\n Xia Song and\n Jianfeng Gao and\n Saurabh Tiwary and\n Rangan Majumder and\n Li Deng},\n title = {{MS} {MARCO:} {A} Human Generated MAchine Reading COmprehension Dataset},\n journal = {CoRR},\n volume = {abs/1611.09268},\n year = {2016},\n url = {http://arxiv.org/abs/1611.09268},\n archivePrefix = {arXiv},\n eprint = {1611.09268},\n timestamp = {Mon, 13 Aug 2018 16:49:03 +0200},\n biburl = {https://dblp.org/rec/journals/corr/NguyenRSGTMD16.bib},\n bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n}\n",
"homepage": "https://microsoft.github.io/msmarco/",
"license": "",
"features": {
"answers": {
"feature": {
"dtype": "string",
"_type": "Value"
},
"_type": "Sequence"
},
"passages": {
"feature": {
"is_selected": {
"dtype": "int32",
"_type": "Value"
},
"passage_text": {
"dtype": "string",
"_type": "Value"
},
"url": {
"dtype": "string",
"_type": "Value"
}
},
"_type": "Sequence"
},
"query": {
"dtype": "string",
"_type": "Value"
},
"query_id": {
"dtype": "int32",
"_type": "Value"
},
"query_type": {
"dtype": "string",
"_type": "Value"
},
"wellFormedAnswers": {
"feature": {
"dtype": "string",
"_type": "Value"
},
"_type": "Sequence"
}
},
"builder_name": "ms_marco",
"dataset_name": "ms_marco",
"config_name": "v1.1",
"version": {
"version_str": "1.1.0",
"description": "",
"major": 1,
"minor": 1,
"patch": 0
},
"splits": {
"validation": {
"name": "validation",
"num_bytes": 42665198,
"num_examples": 10047,
"dataset_name": null
},
"train": {
"name": "train",
"num_bytes": 350516260,
"num_examples": 82326,
"dataset_name": null
},
"test": {
"name": "test",
"num_bytes": 40977580,
"num_examples": 9650,
"dataset_name": null
}
},
"download_size": 217328153,
"dataset_size": 434159038,
"size_in_bytes": 651487191
},
"v2.1": {
"description": "\nStarting with a paper released at NIPS 2016, MS MARCO is a collection of datasets focused on deep learning in search.\n\nThe first dataset was a question answering dataset featuring 100,000 real Bing questions and a human generated answer.\nSince then we released a 1,000,000 question dataset, a natural langauge generation dataset, a passage ranking dataset,\nkeyphrase extraction dataset, crawling dataset, and a conversational search.\n\nThere have been 277 submissions. 20 KeyPhrase Extraction submissions, 87 passage ranking submissions, 0 document ranking\nsubmissions, 73 QnA V2 submissions, 82 NLGEN submisions, and 15 QnA V1 submissions\n\nThis data comes in three tasks/forms: Original QnA dataset(v1.1), Question Answering(v2.1), Natural Language Generation(v2.1).\n\nThe original question answering datset featured 100,000 examples and was released in 2016. Leaderboard is now closed but data is availible below.\n\nThe current competitive tasks are Question Answering and Natural Language Generation. Question Answering features over 1,000,000 queries and\nis much like the original QnA dataset but bigger and with higher quality. The Natural Language Generation dataset features 180,000 examples and\nbuilds upon the QnA dataset to deliver answers that could be spoken by a smart speaker.\n\n\nversion v2.1",
"citation": "\n@article{DBLP:journals/corr/NguyenRSGTMD16,\n author = {Tri Nguyen and\n Mir Rosenberg and\n Xia Song and\n Jianfeng Gao and\n Saurabh Tiwary and\n Rangan Majumder and\n Li Deng},\n title = {{MS} {MARCO:} {A} Human Generated MAchine Reading COmprehension Dataset},\n journal = {CoRR},\n volume = {abs/1611.09268},\n year = {2016},\n url = {http://arxiv.org/abs/1611.09268},\n archivePrefix = {arXiv},\n eprint = {1611.09268},\n timestamp = {Mon, 13 Aug 2018 16:49:03 +0200},\n biburl = {https://dblp.org/rec/journals/corr/NguyenRSGTMD16.bib},\n bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n}\n",
"homepage": "https://microsoft.github.io/msmarco/",
"license": "",
"features": {
"answers": {
"feature": {
"dtype": "string",
"_type": "Value"
},
"_type": "Sequence"
},
"passages": {
"feature": {
"is_selected": {
"dtype": "int32",
"_type": "Value"
},
"passage_text": {
"dtype": "string",
"_type": "Value"
},
"url": {
"dtype": "string",
"_type": "Value"
}
},
"_type": "Sequence"
},
"query": {
"dtype": "string",
"_type": "Value"
},
"query_id": {
"dtype": "int32",
"_type": "Value"
},
"query_type": {
"dtype": "string",
"_type": "Value"
},
"wellFormedAnswers": {
"feature": {
"dtype": "string",
"_type": "Value"
},
"_type": "Sequence"
}
},
"builder_name": "ms_marco",
"dataset_name": "ms_marco",
"config_name": "v2.1",
"version": {
"version_str": "2.1.0",
"description": "",
"major": 2,
"minor": 1,
"patch": 0
},
"splits": {
"validation": {
"name": "validation",
"num_bytes": 413765365,
"num_examples": 101093,
"dataset_name": null
},
"train": {
"name": "train",
"num_bytes": 3462807709,
"num_examples": 808731,
"dataset_name": null
},
"test": {
"name": "test",
"num_bytes": 405691932,
"num_examples": 101092,
"dataset_name": null
}
},
"download_size": 2105722550,
"dataset_size": 4282265006,
"size_in_bytes": 6387987556
}
} |