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== Derivatives and other uses == The [[tar (computing)|tar]] utility included in most Linux distributions can extract .tar.gz files by passing the {{mono|z}} option, e.g., {{mono|tar -zxf file.tar.gz}}, where <code>-z</code> instructs decompression, <code>-x</code> means extraction, and <code>-f</code> specifies the name of the compressed archive file to extract from. Optionally, <code>-v</code> (''verbose'') lists files as they are being extracted.<ref>{{cite web |title=How To Extract / Unzip tar.gz Files From Linux Command Line |url=https://phoenixnap.com/kb/extract-tar-gz-files-linux-command-line |website=Knowledge Base by phoenixNAP |access-date=12 January 2022 |date=14 November 2019}}</ref> [[zlib]] is an abstraction of the DEFLATE algorithm in library form which includes support both for the gzip file format and a lightweight [[Data stream#General|data stream]] format in its API. The zlib stream format, DEFLATE, and the gzip file format were standardized respectively as RFC 1950, RFC 1951, and RFC 1952. The gzip format is used in [[HTTP compression]], a technique used to speed up the sending of [[HTML]] and other content on the [[World Wide Web]]. It is one of the three standard formats for HTTP compression as specified in RFC 2616. This [[Request for Comments|RFC]] also specifies a zlib format (called "DEFLATE"), which is equal to the gzip format except that gzip adds eleven bytes of overhead in the form of headers and trailers. Still, the gzip format is sometimes recommended over zlib because [[Internet Explorer]] does not implement the standard correctly and cannot handle the zlib format as specified in RFC 1950.<ref>{{cite web |first=Eric |last=Lawrence |title=Compressing the Web |url=http://blogs.msdn.com/b/ieinternals/archive/2014/10/21/http-compression-optimize-file-formats-with-deflate.aspx |date=21 November 2014 |website=MSDN Blogs > IEInternals |publisher=[[Microsoft]] |access-date=2 November 2015 |archive-date=28 October 2015 |archive-url=https://web.archive.org/web/20151028172035/http://blogs.msdn.com/b/ieinternals/archive/2014/10/21/http-compression-optimize-file-formats-with-deflate.aspx |url-status=live }}</ref> zlib DEFLATE is used internally by the [[Portable Network Graphics]] (PNG) format. Since the late 1990s, [[bzip2]], a file compression utility based on a block-sorting algorithm, has gained some popularity as a gzip replacement. It produces considerably smaller files (especially for source code and other structured text), but at the cost of memory and processing time (up to a factor of 4).<ref name="benchmark">{{cite web |url=http://compressionratings.com/comp.cgi?7-zip+9.12b++bzip2+1.0.5++gzip+1.3.3+-5 |title=Comparison Tool: 7-zip vs bzip2 vs gzip | website=compressionratings.com |access-date=1 November 2014 |url-status=dead |archive-url=https://web.archive.org/web/20141101212919/http://compressionratings.com/comp.cgi?7-zip+9.12b++bzip2+1.0.5++gzip+1.3.3+-5 |archive-date=1 November 2014 }}</ref> AdvanceCOMP, [[Zopfli]], libdeflate and [[7-Zip]] can produce gzip-compatible files, using an internal DEFLATE implementation with better compression ratios than gzip itself—at the cost of more processor time compared to the reference implementation.{{citation needed|date=December 2018}} Research published in 2023 showed that simple lossless compression techniques such as gzip could be combined with a [[K-nearest neighbors algorithm|k-nearest-neighbor classifier]] to create an attractive alternative to [[deep neural networks]] for text classification in [[natural language processing]]. This approach has been shown to equal and in some cases outperform conventional approaches such as [[BERT (language model)|BERT]] due to low resource requirements, e.g. no requirement for [[GPU]] hardware.<ref>{{Cite journal |last1=Jiang |first1=Zhiying |last2=Yang |first2=Matthew |last3=Tsirlin |first3=Mikhail |last4=Tang |first4=Raphael |last5=Dai |first5=Yiqin |last6=Lin |first6=Jimmy |date=July 2023 |title="Low-Resource" Text Classification: A Parameter-Free Classification Method with Compressors |url=https://aclanthology.org/2023.findings-acl.426 |journal=Findings of the Association for Computational Linguistics: ACL 2023 |location=Toronto, Canada |publisher=Association for Computational Linguistics |pages=6810–6828|doi=10.18653/v1/2023.findings-acl.426 |s2cid=260668487 |doi-access=free }}</ref>
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