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Halstead's goal was to identify measurable properties of software, and the relations between them. This is similar to the identification of measurable properties of matter (like the volume, mass, and pressure of a gas) and the relationships between them (analogous to the gas equation). Thus his metrics are actually not just complexity metrics.
The concept of measuring software size was first introduced by Maurice Halstead [2] from Purdue University in 1975. He suggested that every computer program consists mainly of tokens: operators and operands. He concluded that a count of the number of unique operators and operands gives us a measure of the size of the program.
In software engineering and development, a software metric is a standard of measure of a degree to which a software system or process possesses some property. [1] [2] Even if a metric is not a measurement (metrics are functions, while measurements are the numbers obtained by the application of metrics), often the two terms are used as synonyms.
Many of the existing software measures count structural elements of the application that result from parsing the source code for such individual instructions [63] tokens [64] control structures , and objects. [65] Software quality measurement is about quantifying to what extent a system or software rates along these dimensions.
One method of software measurement is metrics that are analyzed against the code itself. These are called software metrics and including simple metrics, such as counting the number of lines in a single file, the number of files in an application, the number of functions in a file, etc.
Suppose the model has parameter count , and after being finetuned on Python tokens, it achieves some loss . We say that its "transferred token count" is D T {\displaystyle D_{T}} , if another model with the same N {\displaystyle N} achieves the same L {\displaystyle L} after training on D F + D T {\displaystyle D_{F}+D_{T}} Python tokens.
The meme token has soared 41% in just the past six weeks. ... At $0.01, Shiba Inu's market value would be a gargantuan $5.9 trillion (based on the current token count), making it more valuable ...
which shows which documents contain which terms and how many times they appear. Note that, unlike representing a document as just a token-count list, the document-term matrix includes all terms in the corpus (i.e. the corpus vocabulary), which is why there are zero-counts for terms in the corpus which do not also occur in a specific document.