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Large dataset that covers a wider range of reasoning abilities Each task consists of input/output, and a task definition. Additionally, each ask contains a task definition. Further information is provided in the GitHub repository of the project and the Hugging Face data card. Input/Output and task definition 2022 [341] Wang et al. LAMBADA
Data loading, or simply loading, is a part of data processing where data is moved between two systems so that it ends up in a staging area on the target system. With the traditional extract, transform and load (ETL) method, the load job is the last step, and the data that is loaded has already been transformed.
Caltech-UCSD Birds-200-2011 Dataset Large dataset of images of birds. Part locations for birds, bounding boxes, 312 binary attributes given 11,788 Images, text Classification 2011 [193] [194] C. Wah et al. YouTube-8M Large and diverse labeled video dataset YouTube video IDs and associated labels from a diverse vocabulary of 4800 visual entities
To avoid this ambiguity, Pandas supports the syntax data.loc['a'] as an alternative way to filter using the index. Pandas also supports the syntax data.iloc[n], which always takes an integer n and returns the nth value, counting from 0. This allows a user to act as though the index is an array-like sequence of integers, regardless of how it's ...
B-trees were invented by Rudolf Bayer and Edward M. McCreight while working at Boeing Research Labs to efficiently manage index pages for large random-access files. The basic assumption was that indices would be so voluminous that only small chunks of the tree could fit in main memory.
Dask is an open-source Python library for parallel computing.Dask [1] scales Python code from multi-core local machines to large distributed clusters in the cloud. Dask provides a familiar user interface by mirroring the APIs of other libraries in the PyData ecosystem including: Pandas, scikit-learn and NumPy.
Before data mining algorithms can be used, a target data set must be assembled. As data mining can only uncover patterns actually present in the data, the target data set must be large enough to contain these patterns while remaining concise enough to be mined within an acceptable time limit. A common source for data is a data mart or data ...
The word2vec algorithm estimates these representations by modeling text in a large corpus. Once trained, such a model can detect synonymous words or suggest additional words for a partial sentence. Word2vec was developed by Tomáš Mikolov and colleagues at Google and published in 2013.