Datasets:
Tasks:
Text Generation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
License:
Upload dataset_infos.json with huggingface_hub
Browse files- dataset_infos.json +4 -4
dataset_infos.json
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{"default": {
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"description": "LogicNLG
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"citation": "@inproceedings{chen2020logical,\n title={Logical Natural Language Generation from Open-Domain Tables},\n author={Chen, Wenhu and Chen, Jianshu and Su, Yu and Chen, Zhiyu and Wang, William Yang},\n booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},\n pages={7929--7942},\n year={2020}\n}\n",
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"homepage": "https://wenhuchen.github.io/logicnlg.github.io/",
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"license": "",
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"features": {
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"table": {
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"dtype": "large_string",
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"builder_name": "logicnlg",
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"config_name": "default",
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"version": {
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"version_str": "
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"major":
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"minor": 0,
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"patch": 0
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},
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{"default": {
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"description": "LogicNLG is a dataset for natural language generation from open-domain tables. \nLogicNLG is based on TabFact (Chen et al., 2019), which is a table-based fact-checking dataset with rich logical inferences in the annotated statements.\n",
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"citation": "@inproceedings{chen2020logical,\n title={Logical Natural Language Generation from Open-Domain Tables},\n author={Chen, Wenhu and Chen, Jianshu and Su, Yu and Chen, Zhiyu and Wang, William Yang},\n booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},\n pages={7929--7942},\n year={2020}\n}\n",
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"homepage": "https://wenhuchen.github.io/logicnlg.github.io/",
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"license": "MIT",
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"features": {
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"table": {
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"dtype": "large_string",
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"builder_name": "logicnlg",
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"config_name": "default",
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"version": {
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"version_str": "1.0.0",
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"major": 1,
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"minor": 0,
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"patch": 0
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},
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