Saravanan666/pypi-org huggingface dataset:
Package: arti
artigraphDeclarative Data ProductionArtigraph is a tool to improve the authorship, management, and quality of data. It emphasizes that the core deliverable of a data pipeline or workflow is the data, not the tasks.Artigraph is hosted by theLF AI and Data Foundationas a Sandbox project.InstallationArtigraph can be installed from PyPI on python 3.9+ withpip install arti.ExampleThis sample from thespend examplehighlights computing the total amount spent from a series of purchase transactions:frompathlibimportPathfromtypingimportAnnotatedfromartiimportAnnotation,Artifact,Graph,producerfromarti.formats.jsonimportJSONfromarti.storage.localimportLocalFilefromarti.typesimportCollection,Date,Float64,Int64,Structfromarti.versionsimportSemVerDIR=Path(__file__).parentclassVendor(Annotation):name:strclassTransactions(Artifact):"""Transactions partitioned by day."""type=Collection(element=Struct(fields={"id":Int64(),"date":Date(),"amount":Float64()}),partition_by=("date",),)classTotalSpend(Artifact):"""Aggregate spend over all time."""type=Float64()format=JSON()storage=LocalFile()@producer(version=SemVer(major=1,minor=0,patch=0))defaggregate_transactions(transactions:Annotated[list[dict],Transactions])->Annotated[float,TotalSpend]:returnsum(txn["amount"]fortxnintransactions)withGraph(name="test-graph")asg:g.artifacts.vendor.transactions=Transactions(annotations=[Vendor(name="Acme")],format=JSON(),storage=LocalFile(path=str(DIR/"transactions"/"{date.iso}.json")),)g.artifacts.spend=aggregate_transactions(transactions=g.artifacts.vendor.transactions)The full example can be run easily withdocker run --rm artigraph/example-spend:INFO:root:Writing mock Transactions data:
INFO:root:      /usr/src/app/transactions/2021-10-01.json: [{'id': 1, 'amount': 9.95}, {'id': 2, 'amount': 7.5}]
INFO:root:      /usr/src/app/transactions/2021-10-02.json: [{'id': 3, 'amount': 5.0}, {'id': 4, 'amount': 12.0}, {'id': 4, 'amount': 7.55}]
INFO:root:Building aggregate_transactions(transactions=Transactions(format=JSON(), storage=LocalFile(path='/usr/src/app/transactions/{date.iso}.json'), annotations=(Vendor(name='Acme'),)))...
INFO:root:Build finished.
INFO:root:Final Spend data:
INFO:root:      /tmp/test-graph/spend/7564053533177891797/spend.json: 42.0CommunityEveryone is welcome to join the community - learn more in outsupportandcontributingpages!Presentations2022-01-27: Requesting Sandbox Incubation withLF AI & Data(deck,presentation@ 6m35s)
Package: article-crawler
Article CrawlerEnglish Doc|中文文档✨ IntroductionArticle Crawler is a package used to crawl articles with Markdown format from a specific webpage and store them locally in HTML / Markdown formats.🚀 Quick StartInstall throughpippipinstallarticle-crawlerUsageUsage:python3 -m article_crawler -u [url] -t [type] -o [output_folder] -c [class_] -i [id]Options:
  --version             show program's version number and exit
  -h, --help            show this help message and exit
  -u URL, --url=URL     crawled url (required)
  -t TYPE, --type=TYPE  crawled article type [csdn] | [juejin] | [zhihu] | [jianshu]
  -o OUTPUT_FOLDER, --output_folder=OUTPUT_FOLDER
                        output html / markdown / pdf folder (required)
  -w WEBSITE_TAG, --website_tag=WEBSITE_TAG
                        position of the article content in HTML (not required if 'type' is specified)
  -c CLASS_, --class=CLASS_
                        position of the article content in HTML (not required if 'type' is specified)
  -i ID, --id=ID        position of the article content in HTML (not required if 'type' is specified)type: Specific websites, currently supported are CSDN, Zhihu, Juejin, and Jianshu.website_tag / class_ / id:e.g.<div id="article_content" class="article_content clearfix"></div>In this element,website_tag,class_,idisdiv,article_content clearfix,article_contentrespectively.You don't need to specifytypewhen you specifywebsite_tag / class_ / id.You need to use the web console to locate the position of the article.website_tag / class_ / idis used to locate the position of the article in HTML. It is possible to only use one or two of them instead of all.Open Source LicenseMIT License seehttps://opensource.org/license/mit/
Package: articleDateExtract
AboutarticleDateExtract (Article Date Extract) is a simple open source Python module which is an extension of thearticleDateExtracor, built and maintained byKrishna Kishore, that automatically detects, extracts and normalizes the publication date of an online article or blog post.FeatureExtracting the publication date information when it is specified in a web page, with over 95% success rate.A Quick ExampleimportarticleDateExtractd=articleDateExtract.extractArticlePublishedDate("http://edition.cnn.com/2015/11/28/opinions/sutter-cop21-paris-preview-two-degrees/index.html")print(d)d=articleDateExtract.extractArticlePublishedDate("http://techcrunch.com/2015/11/29/tyro-payments/")print(d)InstallingAvailable through pip:$pipinstallarticleDateExtractAlternatively, you can install from source:$gitclonehttps://github.com/blueshirtdeveloper/article-date-extract.git$cdarticle-date-extract$pythonsetup.pyinstallDependenciesbeautifulsoup4>= 4.9.3python-dateutil>= 2.7.3lxml>= 4.6.1About Krishna KishoreI'm Krishna Kishore. I work as a full-time programmer. In my spare time I do open-sourcing. we crawl the news sites so the need for a scalable solution that will automatically extract and structure the unstructured web is critical. We use multiple signals and algorithms to automatically detect where the post text is, the author name, the comments,
and of course the date. With articleDateExtract (Article Date Extract) we rely on the many "different types of standards" out there to automatically detect the date (with a success rate of over 95%).
Package: articleDateExtractor
[![version][pypi-version]][pypi-url][![License][pypi-license]][license-url][![Downloads][pypi-downloads]][pypi-url][![Gitter][gitter-image]][gitter-url]About=====articleDateExtractor (Article Date Extractor) is a simple open source Python module, built and maintained by [Webhose.io](https://webhose.io), that automatically detects, extracts and normalizes the publication date of an online article or blog post.## Feature1.  Extracting the publication date information when it is specified in a web page, with over 90% success rate.## A Quick Example```pythonimport articleDateExtractord = articleDateExtractor.extractArticlePublishedDate("http://edition.cnn.com/2015/11/28/opinions/sutter-cop21-paris-preview-two-degrees/index.html")print (d)d = articleDateExtractor.extractArticlePublishedDate("http://techcrunch.com/2015/11/29/tyro-payments/")print (d)```## InstallingAvailable through pip:```bash$ pip install articleDateExtractor```Alternatively, you can install from source:```bash$ git clone https://github.com/Webhose/article-date-extractor$ cd article-date-extractor$ python setup.py install```## Dependencies* [beautifulsoup4](http://www.crummy.com/software/BeautifulSoup/bs4/) >= 4.6.0* [python-dateutil](https://github.com/dateutil/dateutil/) >= 2.4.2## About Webhose.ioAt [Webhose.io](https://webhose.io) we crawl, structure, unify and aggregate data from millions of online sources (news sites, blogs, discussion forums, comments etc..), so the need for ascalable solution that will automatically extract and structure the unstructured web is critical. We use multiple signals and algorithms to automatically detect where the post text is, the author name, the comments,and of course the date. With articleDateExtractor (Article Date Extractor) we rely on the many "different types of standards" out there to automatically detect the date (with a success rate of over 90%).[license-url]: https://github.com/Webhose/article-date-extractor/blob/master/LICENSE[gitter-url]: https://gitter.im/Webhose[gitter-image]: https://img.shields.io/badge/Gitter-Join%20Chat-blue.svg?style=flat[pypi-url]: https://pypi.python.org/pypi/articleDateExtractor[pypi-license]: https://img.shields.io/pypi/l/articleDateExtractor.svg?style=flat[pypi-version]: https://img.shields.io/pypi/v/articleDateExtractor.svg?style=flat[pypi-downloads]: https://img.shields.io/pypi/dm/articleDateExtractor.svg?style=flat
Package: article-extraction
Article extraction library.article-extraction is a package that can be used to extract the article content
from an HTML page.InstallationUse poetry to install the library from GitHub.poetryadd"git+https://github.com/pmatigakis/article-extraction.git"UsageExtract the content of an article using article-extraction.fromurllib.requestimporturlopenfromarticles.mss.extractorsimportMSSArticleExtractordocument=urlopen("https://www.bbc.com/sport/formula1/64983451").read()article_extractor=MSSArticleExtractor()article=article_extractor.extract_article(document)print(article)
Package: articlequality
Wikipedia article quality classificationThis library provides a set of utilities for performing automatic detection of
assessment classes of Wikipedia articles.  For more information, see the full
documentation athttps://articlequality.readthedocs.io.Compatible with Python 3.x only.Sorry.Install:pip install articlequalityModels:https://github.com/wikimedia/articlequality/tree/master/modelsDocumentation:https://articlequality.readthedocs.ioBasic usage>>> import articlequality
>>> from revscoring import Model
>>>
>>> scorer_model = Model.load(open("models/enwiki.nettrom_wp10.gradient_boosting.model", "rb"))
>>>
>>> text = "I am the text of a page.  I have a <ref>word</ref>"
>>> articlequality.score(scorer_model, text)
{'prediction': 'stub',
 'probability': {'stub': 0.27156163795807853,
                 'b': 0.14707452309674252,
                 'fa': 0.16844898943510833,
                 'c': 0.057668704007171959,
                 'ga': 0.21617801281707663,
                 'start': 0.13906813268582238}}InstallRequirementsPython 3.5, 3.6 or 3.7All the system requirements ofrevscoringInstallation stepsclone this repositoryinstall the package itself and its dependenciespython setup.py installYou can verify that your installation worked by runningmake enwiki_modelsto build the English Wikipedia article quality model ormake wikidatawiki_modelsto build the item quality model for WikidataRetraining the modelsTo retrain a model, runmake -B MODELe.g.make -B wikidatawiki_models. This will redownload the labels, re-extract the features from the revisions, and then retrain and rescore the model.To skip re-downloading the training labels and re-extracting the features, it is enoughtouchthe files in thedatasets/directory and run themakecommand without the-Bflag.Running testsExample:pytest -vv tests/feature_lists/test_wikidatawiki.pyAuthorsAaron Halfaker --https://github.com/halfakMorten Warncke-Wang --https://github.com/nettrom
Package: articlesumm
The summarization of research articles is a complex task compared to general-purpose summaries.This is a result of
the distict nature or semantic structure of these scientific articles. The presence of inline citations and summarization
modules bias to certain Text features that often work well on less-tecnical text but fail to produce coherence in
this area are all underlying factors.We circumvent these challenges in order to produce more coherent, human-understandable summaries of manuscripts and research text
using this libary.InstallationYou can easily install the package using thepipcommand:pip install articlesummUsageThe package takes a string as input(specify a path/directory for an article or alternatively pass a string as a variable). The tokenization of the sentences and words can be performed with the first function:parse=purge(text)
  type(parse) 

  #tupleAlternatively, you can tokenize the sentences and words with any other technique and pass the processed text to the summarization model.Exampletext='''TextRank is a graph-based ranking model for text processing which can be used in order to find the most relevant sentences in text and also to find keywords. The algorithm is explained in detail in the paper at https://web.eecs.umich.edu/~mihalcea/papers/mihalcea.emnlp04.pdf . In order to find the most relevant sentences in text, a graph is constructed where the vertices of the graph represent each sentence in a document and the edges between sentences are based on content overlap, namely by calculating the number of words that 2 sentences have in common.'''

  from ArticleSumm import purge
  from ArticleSumm import summarizer

  parse=purge(text)

  #summary=summarizer(text,parse[0], parse[1], summary_length=3)
  summary=summarizer(text,words=purge(text)[0], sentence_list=purge(text)[1], summary_length=3)
  print(summary)
Package: articolare
Articolare Python ClientArticolare is a Python client for the Articolare API. It is designed to facilitate interactions with the API, providing a simple and intuitive interface for creating and managing requests.InstallationTo install the Articolare Python client, you can use pip:Copy codepip install articolareUsageFirst, you need to initialize the ArticolareClient with your API key:from articolare import createclient = create.ArticolareClient(api_key="your-api-key"))CreateYou can then use the create method to make a request to the API:languages available:EnglishPortugueseSpanishItalianTokens:Max Tokens = 1000 - If you require more tokens, please refer to our documentation onhttps://www.articolare.com/response = client.create(
    model="art-translator",
    lang="english",
    style="conversation",
    dialect="european"
    prompt="Enter your prompt here",
    max_tokens=1000
)The create method returns a dictionary containing the response data from the API.TestingThis library includes a basic test suite, which you can run using pytest:pytestpytestLicenseThe Articolare library is owned and managed by Articolare ltd.
Package: articon
articon:icon mosaicking art in pythonDescriptionPython module for flexible, corpus-based icon mosaicking. Here are some emoji-art examples made witharticon.AcknowledgementsThis code is a Python porting and expansion ofemoji-mosaicbyericandrewlewis, and inspired byYung Jake'semoji art.InstallationTo install the latest version ofarticonwith pip, run:pipinstallarticonDatasetsA convenient corpus dataset that works well witharticonisKaggle'sFull Emoji Image Dataset, which you can downloadhere.LicenseISC License
Copyright (c) 2023, Felipe Tovar-HenaoPermission to use, copy, modify, and/or distribute this software for any purpose with or without fee is hereby granted, provided that the above copyright notice and this permission notice appear in all copies.THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
Package: articubench
A benchmark to evaluate articulatory speech synthesis systems. This benchmark
uses the VocalTractLab [1] as its articulatory speech synthesis simulator.WarningThis package is not released yet and will be released in March 2022
alongside the ESSV 2022 conference.Types of datawave form (acoustics)log-melspectrogramms (acoustics)formant transitions (acoustics)fasttext 300 dim semantic vector for single words (semantics)mid sagital tongue movement contour from ultra sound imagingelectromagnetic articulatory (EMA) sensors on tongue tip and tongue bodyLanguagesGermanEnglish (planned)Mandarin (planned)VariantsAs running the benchmark is computational itensive there are different versions
of this benchmark, which require different amounts of articulatory synthesis.TinyThe smallest possible benchmark to check that everything works, but with no
statistical power.SmallA small benchmark with some statistical power.NormalThe standard benchmark, which might take some time to complete.CorporaData used here comes from the following speech corpora:GECO (only phonetic transscription; duration and phone)KEC (EMA data, acoustics)Mozilla Common Voicebaba-babi-babu speech rate (ultra sound; acoustics)PrerequisitsFor running the benchmark:python >=3.8praatFor creating the benchmark:mfa (Montreal forced aligner)VTL 2.3 (included in this repository)LicenseVTL is GPLv3.0+ licenseLinks[1]https://www.vocaltractlab.de/
Package: articulo
ArticuloTiny library for extraction articles from html.It can extract the content of an article, both in text and HTML, and it's title.UsageBasic usageThis library is designed to be as simple as possible.To start using it just import it and instantiate with link you want to parse as a parameter.Also the library designed to work in lazy manner.So, until you make a request for some property, it does not send any requests.fromarticuloimportArticulo# Step 1: initializing Articulo instancearticle=Articulo('https://info.cern.ch/')# Step 2: requesting article properties. All properties resolve lazily.print(article.title)# article title as a stringprint(article.text)# article content as a stringprint(article.markup)# article content as an html markup stringprint(article.icon)# link to article iconprint(article.description)# article meta descriptionprint(article.preview)# link to article meta preview imageprint(article.keywords)# article meta keywords listVerbose modeIn case you want to see the whole procees just provide parameterverbose=Trueto the instance. It can be helpful for debugging.fromarticuloimportArticulo# Initializing Articulo instance with verbose modearticle=Articulo('https://info.cern.ch/',verbose=True)Controlling information loss coefficientThe whole idea of parsing article content is to define the part of the document that has the highest information density. To find that part there is the so-calledinformation loss coefficient. This coefficient determines the decrease in the text density of the document during parsing.The default value is0.7which stands for70%information density decrease. In most cases, this works fine.Nevertheless, you can change it in case you have insufficient parsing results. Just providethesholdparameter to thearticuloinstance, it might help.fromarticuloimportArticulo# Initializing Articulo instance with information loss coefficient of 30%article=Articulo('https://info.cern.ch/',threshold=0.3)Providing headersIn some cases  you need to provide additional headers to get an article html from url.For that case you can provide headers withhttp_headersparameter when
you create new instance ofarticulo.fromarticuloimportArticulo# Initializing Articulo instance with custom user agentheaders={'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36'}article=Articulo('https://info.cern.ch/',http_headers=headers)Providing custom charsetArticulo usesrequestslibrary to get html from url. This library tries to guess the encoding of the response based on the HTTP headers.
Although it works fine most of the time, in some cases this might not work as expected, and you'll get a mess instead of text. For that case you can provide custom charset withdef_charsetparameter when you create new instance ofarticulo.fromarticuloimportArticulo# Initializing Articulo instance with cp1251 charsetarticle=Articulo('https://info.cern.ch/',def_charset='cp1251')
Package: artifact-generator
generatorsOntology Based System EngineeringInstallationpipinstallgeneratorsInstallation for development purposespip install -e .Usagegen --helpContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.Authors and acknowledgmentFor readme file I used format fromhttps://www.makeareadme.com/LicenseGNU GPLv3
Package: artifact-hub
artifact-hubManage artifacts using object storage. Manage all kinds of artifacts E.g. models, json files, sqlite dbs and track their
versions and history using any object store as a backend. This project facilitates self-hosted hub without a lot of
management overhead.Currently supported object stores include:S3Installationpipinstallartifact-hubUsageCreate a sample filesample.txtThis is a sample file 
with multiline contentInitialize the hub instance for a specific object/projectfromartifact_hubimports3_hubhub=s3_hub("bucket","folder","object_id")hub.push("sample.txt","first push","description of first push")LimitationsConcurrent push and pulls from multiple parties at the same time can not be handled. because of the linear nature of
the history the last push will overwrite the previous one.Actor A and B tries to push concurrently
Actor A and B pull latest version 10
Actor A complete push version 11 early
Actor B completes push version 11 late and overwrite Actor A's push. This may happen when Actor B is pushing a much larger change than A's
Package: artifacts-keyring
NOTE'artifacts-keyring' is a relatively thin wrapper aroundartifacts-credprovider.  Make sure to also look at that repository for more information about different scenarios. For example:Environment variable to explicitly override tokensSafely using credentials in dockerartifacts-keyringTheartifacts-keyringpackage provides authentication for publishing or consuming Python packages to or from Azure Artifacts feeds withinAzure DevOps.This package is an extension tokeyring, which will automatically find and use it once installed.Bothpipandtwinewill usekeyringto
find credentials.InstallationTo install this package, run the followingpipcommand:pip install artifacts-keyringUsageRequirementsTo useartifacts-keyringto set up authentication betweenpip/twineand Azure Artifacts, the following requirements must be met:pip version19.2or highertwine version1.13.0or higherpython version3.0or higher.Net SDK is installed. Refer toherefor installation guideline.Publishing packages to an Azure Artifacts feedOnceartifacts-keyringis installed, to publish a package, use the followingtwinecommand, replacing<org_name>and<feed_name>with your own:twine upload --repository-url https://pkgs.dev.azure.com/<org_name>/_packaging/<feed_name>/pypi/upload <package_wheel_or_other_dist_format>Installing packages from an Azure Artifacts feedOnceartifacts-keyringis installed, to consume a package, use the followingpipcommand, replacing<org_name>and<feed_name>with your own, and<package_name>with the package you want to install:pip install <package_name> --index-url https://pkgs.dev.azure.com/<org_name>/_packaging/<feed_name>/pypi/simpleAdvanced configurationTheartifacts-keyringpackage is layered on top of ourAzure Artifacts Credential Provider. Check out that link to the GitHub repo for more information on configuration options.Environment variablesARTIFACTS_KEYRING_NONINTERACTIVE_MODE: Controls whether the underlying credential provider can issue interactive prompts.ContributingThis project welcomes contributions and suggestions.  Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visithttps://cla.microsoft.com.When you submit a pull request, a CLA-bot will automatically determine whether you need to provide
a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions
provided by the bot. You will only need to do this once across all repos using our CLA.This project has adopted theMicrosoft Open Source Code of Conduct.
For more information see theCode of Conduct FAQor
contactopencode@microsoft.comwith any additional questions or comments.