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Accepting natural language questions makes the system more user-friendly, but harder to implement, as there are a variety of question types and the system will have to identify the correct one in order to give a sensible answer. Assigning a question type to the question is a crucial task; the entire answer extraction process relies on finding ...
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 ...
In the above example of the usage of exponential mechanism, one can output a synthetic dataset in a differentially private manner and can use the dataset to answer queries with good accuracy. Other private mechanisms, such as posterior sampling, [6] which returns parameters rather than datasets, can be made equivalent to the exponential one. [7]
The HAI researchers, who included a Stanford Law professor, created a dataset of 200 questions designed to mimic the kinds of questions a lawyer might ask a legal research copilot.
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]
Adversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks. [1] A survey from May 2020 revealed practitioners' common feeling for better protection of machine learning systems in industrial applications.
The gay panic defense or homosexual advance defense is a victim blaming strategy of legal defense, which refers to a situation in which a heterosexual individual charged with a violent crime against a homosexual (or bisexual) individual claims they lost control and reacted violently because of an unwanted sexual advance that was made upon them.