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The Stack Exchange dataset is a collection of data from various Stack Exchange sites, including Stack Overflow, Mathematics, Super User, and many others. It includes questions, answers, comments, tags, and other related data from these sites. The dataset is updated regularly and can be accessed through the Stack Exchange Data Explorer. It's also hosted by the Internet Archive and is updated every three months. Additionally, it's available on platforms like Google BigQuery and Kaggle.

SEDE is a dataset comprised of 12,023 complex and diverse SQL queries and their natural language titles and descriptions, written by real users of the Stack Exchange Data Explorer out of a natural interaction. These pairs contain a variety of real-world challenges which were rarely reflected so far in any other semantic parsing dataset. The goal of this dataset is to take a significant step towards evaluation of Text-to-SQL models in a real-world setting. Compared to other Text-to-SQL datasets, SEDE contains at least 10 times more SQL queries templates (queries after canonization and anonymization of values) than other datasets, and has the most diverse set of utterances and SQL queries (in terms of 3-grams) out of all single-domain datasets. SEDE introduces real-world challenges, such as under-specification, usage of parameters in queries, dates manipulation and more.

Description Dataset from the Law Stack Exchange, as used in "Parameter-Efficient Legal Domain Adaptation" (Li et al., 2022). We introduce a dataset with data from the Law Stack Exchange. This dataset is composed of questions from the Law Stack Exchange, which is a community forum-based website containing questions with answers to legal questions. We link the questions with their associated tags (e.g., "copyright" or "criminal-law"), and perform a multi-label classification task ## Citation Information @inproceedings{li-etal-2022-parameter, title = "Parameter-Efficient Legal Domain Adaptation", author = "Li, Jonathan and Bhambhoria, Rohan and Zhu, Xiaodan", booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2022", month = dec, year = "2022", address = "Abu Dhabi, United Arab Emirates (Hybrid)", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.nllp-1.10", pages = "119--129", }

Click to add a brief description of the dataset (Markdown and LaTeX enabled). Provide: * a high-level explanation of the dataset characteristics * explain motivations and summary of its coThe problem of personalization in Information Retrieval has been under study for a long time. A well know issue related to this task is the lack of publicly available datasets that can support a comparative evaluation of personalised search systems. To contribute in this respect, this paper introduces SE-PEF (StackExchange - Personalized Expert Finding), a resource useful for designing and evaluating personalized models related to the task of Expert Finding (EF). The contributed dataset includes more than 250k queries and 565k answers from 3,306 experts, which are annotated with a rich set of features modeling the social interactions among the users of a popular cQA platform. The results of the preliminary experiments conducted show the appropriateness of SE-PEF to evaluate and to train effective EF models.ntent * potential use cases of the dataset