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  2. List of datasets for machine-learning research - Wikipedia

    en.wikipedia.org/wiki/List_of_datasets_for...

    Question-answer pairs Question Answering/Machine Reading Comprehension 2020 [335] Nguyen et al. Open-Domain Question Answering Goes Conversational via Question Rewriting 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 ...

  3. Semantic parsing - Wikipedia

    en.wikipedia.org/wiki/Semantic_parsing

    Semantic Parsing for Conversational Question Answering. A standard dataset for question answering via semantic parsing is the Air Travel Information System (ATIS) dataset, which contains questions and commands about upcoming flights as well as corresponding SQL. [30]

  4. Training, validation, and test data sets - Wikipedia

    en.wikipedia.org/wiki/Training,_validation,_and...

    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]

  5. Question answering - Wikipedia

    en.wikipedia.org/wiki/Question_answering

    Question answering systems in the context of [vague] machine reading applications have also been constructed in the medical domain, for instance related to [vague] Alzheimer's disease. [3] Open-domain question answering deals with questions about nearly anything and can only rely on general ontologies and world knowledge. Systems designed for ...

  6. deepset - Wikipedia

    en.wikipedia.org/wiki/Deepset

    Haystack is an open source Python framework for building custom applications with large language models.With its modular building blocks, software developers can implement pipelines to address various search tasks over large document collections, such as document retrieval, semantic search, text generation, question answering, or summarization.

  7. Natural Language Toolkit - Wikipedia

    en.wikipedia.org/wiki/Natural_Language_Toolkit

    The Natural Language Toolkit, or more commonly NLTK, is a suite of libraries and programs for symbolic and statistical natural language processing (NLP) for English written in the Python programming language. It supports classification, tokenization, stemming, tagging, parsing, and semantic reasoning functionalities. [4]

  8. Data set - Wikipedia

    en.wikipedia.org/wiki/Data_set

    Various plots of the multivariate data set Iris flower data set introduced by Ronald Fisher (1936). [1]A data set (or dataset) is a collection of data.In the case of tabular data, a data set corresponds to one or more database tables, where every column of a table represents a particular variable, and each row corresponds to a given record of the data set in question.

  9. Prompt engineering - Wikipedia

    en.wikipedia.org/wiki/Prompt_engineering

    The question vectors are clustered. Questions nearest to the centroids of each cluster are selected. An LLM does zero-shot CoT on each question. The resulting CoT examples are added to the dataset. When prompted with a new question, CoT examples to the nearest questions can be retrieved and added to the prompt.