Datasets:

microsoft
/

xglue

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(QAM)\n- Question Generation (QG)\n- News Title Generation (NTG)\n\nFor more information, please take a look at https://microsoft.github.io/XGLUE/.\n", "citation": "@misc{11234/1-3105,\n title={Universal Dependencies 2.5},\n author={Zeman, Daniel and Nivre, Joakim and Abrams, Mitchell and Aepli, et al.},\n url={http://hdl.handle.net/11234/1-3105},\n note={{LINDAT}/{CLARIAH}-{CZ} digital library at the Institute of Formal and Applied Linguistics ({{'U}FAL}), Faculty of Mathematics and Physics, Charles University},\n copyright={Licence Universal Dependencies v2.5},\n year={2019}\n}\n@article{Liang2020XGLUEAN,\n title={XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation},\n author={Yaobo Liang and Nan Duan and Yeyun Gong and Ning Wu and Fenfei Guo and Weizhen Qi\n and Ming Gong and Linjun Shou and Daxin Jiang and Guihong Cao and Xiaodong Fan and Ruofei\n Zhang and Rahul Agrawal and Edward Cui and Sining Wei and Taroon Bharti and Ying Qiao\n and Jiun-Hung Chen and Winnie Wu and Shuguang Liu and Fan Yang and Daniel Campos\n and Rangan Majumder and Ming Zhou},\n journal={arXiv},\n year={2020},\n volume={abs/2004.01401}\n}\n", "homepage": "https://universaldependencies.org/", "license": "", "features": {"words": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "pos": {"feature": {"num_classes": 17, "names": ["ADJ", "ADP", "ADV", "AUX", "CCONJ", "DET", "INTJ", "NOUN", "NUM", "PART", "PRON", "PROPN", "PUNCT", "SCONJ", "SYM", "VERB", "X"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "x_glue", "config_name": "pos", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 7279459, "num_examples": 25376, "dataset_name": "x_glue"}, "validation.en": {"name": 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performance of cross-lingual pre-trained\nmodels with respect to cross-lingual natural language understanding and generation.\nThe benchmark is composed of the following 11 tasks:\n- NER\n- POS Tagging (POS)\n- News Classification (NC)\n- MLQA\n- XNLI\n- PAWS-X\n- Query-Ad Matching (QADSM)\n- Web Page Ranking (WPR)\n- QA Matching (QAM)\n- Question Generation (QG)\n- News Title Generation (NTG)\n\nFor more information, please take a look at https://microsoft.github.io/XGLUE/.\n", "citation": "@article{Lewis2019MLQAEC,\n title={MLQA: Evaluating Cross-lingual Extractive Question Answering},\n author={Patrick Lewis and Barlas Oguz and Ruty Rinott and Sebastian Riedel and Holger Schwenk},\n journal={ArXiv},\n year={2019},\n volume={abs/1910.07475}\n}\n@article{Liang2020XGLUEAN,\n title={XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation},\n author={Yaobo Liang and Nan Duan and Yeyun Gong and Ning Wu and Fenfei Guo and Weizhen Qi\n and Ming Gong and 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