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An end-to-end open-domain question answering. This dataset includes 14,000 conversations with 81,000 question-answer pairs. Context, Question, Rewrite, Answer, Answer_URL, Conversation_no, Turn_no, Conversation_source Further details are provided in the project's GitHub repository and respective Hugging Face dataset card. Question Answering ...
Question answering (QA) is a computer science discipline within the fields of information retrieval and natural language processing (NLP) that is concerned with building systems that automatically answer questions that are posed by humans in a natural language.
Another benchmark dataset is the GeoQuery dataset which contains questions about the geography of the U.S. paired with corresponding Prolog. [27] The Overnight dataset is used to test how well semantic parsers adapt across multiple domains; it contains natural language queries about 8 different domains paired with corresponding λ-DCS ...
Some examples of commonly used question answering datasets include TruthfulQA, Web Questions, TriviaQA, and SQuAD. [126] Evaluation datasets may also take the form of text completion, having the model select the most likely word or sentence to complete a prompt, for example: "Alice was friends with Bob. Alice went to visit her friend, ____". [1]
The Stanford team claims their questions are a better test of how legal copilots may perform in a real-world setting than bar exam questions—especially because a lot of datasets of bar exam ...
Large dataset of images for object classification. Images categorized and hand-sorted. 30,607 Images, Text Classification, object detection 2007 [29] [30] G. Griffin et al. COYO-700M Image–text-pair dataset 10 billion pairs of alt-text and image sources in HTML documents in CommonCrawl 746,972,269 Images, Text Classification, Image-Language ...
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
Common Crawl is a nonprofit 501(c)(3) organization that crawls the web and freely provides its archives and datasets to the public. [1] [2] Common Crawl's web archive consists of petabytes of data collected since 2008. [3] It completes crawls approximately once a month. [4] Common Crawl was founded by Gil Elbaz. [5]