Datasets:
Modalities:
Text
Sub-tasks:
masked-language-modeling
Languages:
English
Size:
1M - 10M
ArXiv:
License:
File size: 4,734 Bytes
25109f8 6c3f9fe dc08ef0 25109f8 6c3f9fe dc08ef0 25109f8 |
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 |
{
"abstracts": {
"description": "Abstracts from TREx dataset",
"citation": "@inproceedings{elsahar2018t, title={T-rex: A large scale alignment of natural language with knowledge base triples}, author={Elsahar, Hady and Vougiouklis, Pavlos and Remaci, Arslen and Gravier, Christophe and Hare, Jonathon and Laforest, Frederique and Simperl, Elena},booktitle={Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year={2018}}",
"homepage": "https://hadyelsahar.github.io/t-rex/",
"license": "Creative Commons Attribution-ShareAlike 4.0 International License. see https://creativecommons.org/licenses/by-sa/4.0/",
"features": {
"inputs_pretokenized": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"targets_pretokenized": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"masked_uri": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"masked_type": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"facts": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"example_uris": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"page_uri": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"supervised_keys": null,
"builder_name": "ftrace",
"config_name": "abstracts",
"version": {
"version_str": "0.0.2",
"description": null,
"datasets_version_to_prepare": null,
"major": 0,
"minor": 0,
"patch": 2
},
"splits": {
"train": {
"name": "train",
"num_bytes": 473823085,
"num_examples": 1560453,
"dataset_name": "abstracts"
}
}
},
"queries": {
"description": "Queries from LAMA dataset",
"citation": "@inproceedings{petroni2019language,title={Language Models as Knowledge Bases?},author={F. Petroni, T. Rockt{\"{a}}schel, A. H. Miller, P. Lewis, A. Bakhtin, Y. Wu and S. Riedel},booktitle={In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019},year={2019}}",
"homepage": "https://github.com/facebookresearch/LAMA",
"license": "The Creative Commons Attribution-Noncommercial 4.0 International License. see https://github.com/facebookresearch/LAMA/blob/master/LICENSE",
"features": {
"inputs_pretokenized": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"targets_pretokenized": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"obj_surface": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sub_surface": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"obj_uri": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sub_uri": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"predicate_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"uuid": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"supervised_keys": null,
"builder_name": "ftrace",
"config_name": "queries",
"version": {
"version_str": "0.0.2",
"description": null,
"datasets_version_to_prepare": null,
"major": 0,
"minor": 0,
"patch": 2
},
"splits": {
"train": {
"name": "train",
"num_bytes": 5426318,
"num_examples": 31479,
"dataset_name": "abstracts"
}
}
}
} |