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Returns a substring of string between starting at startpos and endpos, or starting at startpos of length numChars. The resulting string is truncated if there are fewer than numChars characters beyond the starting point. endpos represents the index after the last character in the substring.
Python sets are very much like mathematical sets, and support operations like set intersection and union. Python also features a frozenset class for immutable sets, see Collection types. Dictionaries (class dict) are mutable mappings tying keys and corresponding values. Python has special syntax to create dictionaries ({key: value})
The bag-of-words model (BoW) is a model of text which uses a representation of text that is based on an unordered collection (a "bag") of words. It is used in natural language processing and information retrieval (IR). It disregards word order (and thus most of syntax or grammar) but captures multiplicity.
Given two strings a and b on an alphabet Σ (e.g. the set of ASCII characters, the set of bytes [0..255], etc.), the edit distance d(a, b) is the minimum-weight series of edit operations that transforms a into b. One of the simplest sets of edit operations is that defined by Levenshtein in 1966: [2] Insertion of a single symbol.
Python 2 also distinguishes two types of strings: 8-bit ASCII ("bytes") strings (the default), explicitly indicated with a b or B prefix, and Unicode strings, indicated with a u or U prefix. [25] while in Python 3 strings are Unicode by default and bytes are a separate bytes type that when initialized with quotes must be prefixed with a b.
A hyphenation algorithm is a set of rules, especially one codified for implementation in a computer program, that decides at which points a word can be broken over two lines with a hyphen. For example, a hyphenation algorithm might decide that impeachment can be broken as impeach-ment or im-peachment but not impe-achment .
Preheat oven to 325°F and line two baking trays with parchment paper. In a large mixing bowl combine peanut butter, milk, and eggs. Then add the flour and baking powder.
The word with embeddings most similar to the topic vector might be assigned as the topic's title, whereas far away word embeddings may be considered unrelated. As opposed to other topic models such as LDA, top2vec provides canonical ‘distance’ metrics between two topics, or between a topic and another embeddings (word, document, or ...