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Although doctest does not allow a Python program to be embedded in narrative text, it does allow for verifiable examples to be embedded in docstrings, where the docstrings can contain other text. Docstrings can in turn be extracted from program files to generate documentation in other formats such as HTML or PDF.
PDFtk (short for PDF Toolkit) is a toolkit for manipulating Portable Document Format (PDF) documents. [ 3 ] [ 4 ] It runs on Linux , Windows and macOS . [ 5 ] It comes in three versions: PDFtk Server ( open-source command-line tool ), PDFtk Free ( freeware ) and PDFtk Pro ( proprietary paid ). [ 2 ]
Supports merging, splitting, and extracting pages from PDFs. Also rotating, deleting and reordering pages. Converts PDF to Word, Excel, PowerPoint, raster images. Soda PDF: Proprietary: Yes Yes Yes Modular PDF software. Solid Converter PDF: Proprietary: Yes Yes Yes PDF to Word, Excel, HTML and Text; supports passwords, text editing, and batch ...
A Byte of Python: Author: Swaroop C H: Software used: DocBook XSL Stylesheets with Apache FOP: Conversion program: Apache FOP Version 1.1: Encrypted: no: Page size: 595.275 x 841.889 pts (A4) Version of PDF format: 1.4
Text segmentation is the process of dividing written text into meaningful units, such as words, sentences, or topics. The term applies both to mental processes used by humans when reading text, and to artificial processes implemented in computers, which are the subject of natural language processing .
This program outputs all words that begin with a capital letter, one word per line, and discards all other text: process submit file "myfile.txt" ; or submit "ANY Text discard lowercase words" ; output capitalized word, append a newline find ( uc letter * ) => temp output temp || "%n" ; discard all other characters find any ; no output
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Word2vec is a technique in natural language processing (NLP) for obtaining vector representations of words. These vectors capture information about the meaning of the word based on the surrounding words. The word2vec algorithm estimates these representations by modeling text in a large corpus.