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Taskmaster-1 and Taskmaster-2: conversation id, utterances, Instruction id Taskmaster-3: conversation id, utterances, vertical, scenario, instructions. For further details check the project's GitHub repository or the Hugging Face dataset cards ( taskmaster-1 , taskmaster-2 , taskmaster-3 ).
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]
In computer science, rate-monotonic scheduling (RMS) [1] is a priority assignment algorithm used in real-time operating systems (RTOS) with a static-priority scheduling class. [2] The static priorities are assigned according to the cycle duration of the job, so a shorter cycle duration results in a higher job priority.
Axios ' s content is designed for digital platforms, such as Facebook and Snapchat, as well as its own website. [2] Its articles are typically less than 300 words long. [21] In addition to its website, Axios content is distributed via newsletters covering politics, technology, healthcare, and other subjects. [22]
The request/response message consists of the following: Request line, such as GET /logo.gif HTTP/1.1 or Status line, such as HTTP/1.1 200 OK, Headers; An empty line; Optional HTTP message body data; The request/status line and headers must all end with <CR><LF> (that is, a carriage return followed by a line feed).
The design matrix contains data on the independent variables (also called explanatory variables), in a statistical model that is intended to explain observed data on a response variable (often called a dependent variable). The theory relating to such models uses the design matrix as input to some linear algebra : see for example linear regression.
For example, a prompt may include a few examples for a model to learn from, such as asking the model to complete "maison → house, chat → cat, chien →" (the expected response being dog), [23] an approach called few-shot learning. [24] In-context learning is an emergent ability [25] of large language models.
The Nested Set model is appropriate where the tree element and one or two attributes are the only data, but is a poor choice when more complex relational data exists for the elements in the tree. Given an arbitrary starting depth for a category of 'Vehicles' and a child of 'Cars' with a child of 'Mercedes', a foreign key table relationship must ...