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The datasets are provided under the original terms that Microsoft received such datasets. See below for more information about each dataset.
The training and evaluation for CNN/DM dataset is available https://s3.amazonaws.com/opennmt-models/Summary/cnndm.tar.gz and released under MIT License. This is a processed version of data that's originally released by Hermann et al. (2015) in "Teaching machines to read and comprehend" and then made available by Kyunghyun Cho at https://cs.nyu.edu/~kcho/DMQA/.
The preprocessed dataset of CNN/DM dataset, originally published by BERTSUM paper "Fine-tune BERT for Extractive Summarization", can be found at https://github.com/nlpyang/BertSum and released under Apache License 2.0.
Original source: https://www.microsoft.com/en-us/download/details.aspx?id=52398
The majority of the corpus is released under the OANC’s license, The data in the FICTION section falls under several permissive licenses. See the data description paper for details. Redistributing the datasets "MultiNLI 1.0.zip", "MultiNLI Matched.zip", and "MultiNLI Mismatched.zip" with attribution: Adina Williams, Nikita Nangia, Samuel R. Bowman. 2018. A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). Original source: https://www.nyu.edu/projects/bowman/multinli/
This dataset is provided under CC BY-SA 4.0. Redistributing the dataset "snli_1.0.zip" with attribution: Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015. A large annotated corpus for learning natural language inference. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP). Original source: https://nlp.stanford.edu/projects/snli/ The dataset is preprocessed to remove unused columns and badly formatted rows.
This dataset is provided under CC BY 4.0. Redistributing the dataset "wikigold.conll.txt" with attribution: Balasuriya, Dominic, et al. "Named entity recognition in wikipedia." Proceedings of the 2009 Workshop on The People's Web Meets NLP: Collaboratively Constructed Semantic Resources. Association for Computational Linguistics, 2009. Original source: https://github.com/juand-r/entity-recognition-datasets/tree/master/data/wikigold/CONLL-format/data The dataset is preprocessed to fit data format requirement of BERT.
The majority of the corpus sentences are released under the OANC’s license. The data in the Fiction genre from Captain Blood are under The_Project_Gutenberg_License. See details in the XNLI paper. Redistributing the datasets "XNLI 1.0.zip" and "XNLI-MT 1.0.zip" with attribution: Alexis Conneau, Guillaume Lample, Ruty Rinott, Holger Schwenk, Ves Stoyanov. 2018. XNLI: Evaluating Cross-lingual Sentence Representations. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Original source: https://www.nyu.edu/projects/bowman/xnli/ The dataset is preprocessed to remove unused columns.
This dataset is provided under CC BY-SA 4.0. Redistributing the datasets "train-v1.1.json" and "dev-v1.1.json" with attribution: Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. SQuAD: 100,000+ Questions for Machine Comprehension of Text. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP). Original source: https://github.com/rajpurkar/SQuAD-explorer
Redistributing the dataset "Stsbenchmark.tar.gz" with attribution: Eneko Agirre, Daniel Cer, Mona Diab, Iñigo Lopez-Gazpio, Lucia Specia. Semeval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation. Proceedings of SemEval 2017. Orignal source:http://ixa2.si.ehu.es/stswiki/index.php/STSbenchmark The dataset is preprocessed to remove unused columns. The scores are released under Commons Attribution - Share Alike 4.0 International License The text of each dataset has a license of its own, as follows:
- MSR-Paraphrase, Microsoft Research Paraphrase Corpus. In order to use MSRpar, researchers need to agree with the license terms from Microsoft Research: http://research.microsoft.com/en-us/downloads/607d14d9-20cd-47e3-85bc-a2f65cd28042/
- headlines: Mined from several news sources by European Media Monitor (Best et al. 2005). using the RSS feed. European Media Monitor (EMM) Real Time News Clusters are the top news stories for the last 4 hours, updated every ten minutes. The article clustering is fully automatic. The selection and placement of stories are determined automatically by a computer program. This site is a joint project of DG-JRC and DG-COMM. The information on this site is subject to a disclaimer (see http://europa.eu/geninfo/legal_notices_en.htm). Please acknowledge EMM when (re)using this material. http://emm.newsbrief.eu/rss?type=rtn&language=en&duplicates=false
- deft-news: A subset of news article data in the DEFT project.
- MSR-Video, Microsoft Research Video Description Corpus. In order to use MSRvideo, researchers need to agree with the license terms from Microsoft Research: http://research.microsoft.com/en-us/downloads/38cf15fd-b8df-477e-a4e4-a4680caa75af/
- image: The Image Descriptions data set is a subset of the PASCAL VOC-2008 data set (Rashtchian et al., 2010) . PASCAL VOC-2008 data set consists of 1,000 images and has been used by a number of image description systems. The image captions of the data set are released under a CreativeCommons Attribution-ShareAlike license, the descriptions itself are free.
- track5.en-en: This text is a subset of the Stanford Natural Language Inference (SNLI) corpus, by The Stanford NLP Group is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Based on a work at http://shannon.cs.illinois.edu/DenotationGraph/. https://creativecommons.org/licenses/by-sa/4.0/
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======ANSWER-ANSWER======
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