Saravanan666/pypi-org huggingface dataset:
Package: uniparser-buryat
Buryat morphological analyzerThis is a rule-based morphological analyzer for Buryat (bua; Mongolic). It is based on a formalized description of literary Buryat morphology, which usesuniparser-morphfor parsing. It performs full morphological analysis of Buryat words (lemmatization, POS tagging, grammatical tagging, glossing).NB: The analyzer is still under construction. Right now, a number of entries in the dictionary have wrong POS tags or paradigms. Use with caution.How to usePython packageThe analyzer is available as a Python package. If you want to analyze Buryat texts in Python, install the module:pip3 install uniparser-buryatImport the module and create an instance ofBuryatAnalyzerclass. Setmode='strict'if you are going to process text in standard orthography, ormode='nodiacritics'if you expect some words to lack the diacritics (which often happens in social media). After that, you can either parse tokens or lists of tokens withanalyze_words(), or parse a frequency list withanalyze_wordlist(). Here is a simple example:fromuniparser_buryatimportBuryatAnalyzera=BuryatAnalyzer(mode='strict')analyses=a.analyze_words('Морфологи')# The parser is initialized before first use, so expect# some delay here (usually several seconds)# You will get a list of Wordform objects# The analysis attributes are stored in its properties# as string values, e.g.:foranainanalyses:print(ana.wf,ana.lemma,ana.gramm,ana.gloss)# You can also pass lists (even nested lists) and specify# output format ('xml' or 'json')# If you pass a list, you will get a list of analyses# with the same structureanalyses=a.analyze_words([['А'],['Би','шамдаа','дуратайб','.']],format='xml')analyses=a.analyze_words(['Морфологи',[['А'],['Би','шамдаа','дуратайб','.']]],format='json')Refer to theuniparser-morph documentationfor the full list of options.Word listsDescription formatThe description is carried out in theuniparser-morphformat and involves a description of the inflection (paradigms.txt), a grammatical dictionary (bua_lexemes_XXX.txt files), and a short list of analyses that should be avoided (bad_analyses.txt). The dictionary contains descriptions of individual lexemes, each of which is accompanied by information about its stem, its part-of-speech tag and some other grammatical/borrowing information, its inflectional type (paradigm), and, for some, Russian translation. See more about the formatin the uniparser-morph documentation.
Package: uniparser-erzya
Erzya morphological analyzerThis is a rule-based morphological analyzer for Erzya (myv; Uralic > Mordvinic). It is based on a formalized description of literary Erzya morphology, which also includes a number of dialectal elements, and usesuniparser-morphfor parsing. It performs full morphological analysis of Erzya words (lemmatization, POS tagging, grammatical tagging, glossing).How to usePython packageThe analyzer is available as a Python package. If you want to analyze Erzya texts in Python, install the module:pip3 install uniparser-erzyaImport the module and create an instance ofErzyaAnalyzerclass. Setmode='strict'if you are going to process text in standard orthography, ormode='nodiacritics'if you expect some words to lack the diacritics (which often happens in social media). After that, you can either parse tokens or lists of tokens withanalyze_words(), or parse a frequency list withanalyze_wordlist(). Here is a simple example:fromuniparser_erzyaimportErzyaAnalyzera=ErzyaAnalyzer(mode='strict')analyses=a.analyze_words('Морфологиянть')# The parser is initialized before first use, so expect# some delay here (usually several seconds)# You will get a list of Wordform objects# The analysis attributes are stored in its properties# as string values, e.g.:foranainanalyses:print(ana.wf,ana.lemma,ana.gramm,ana.gloss)# You can also pass lists (even nested lists) and specify# output format ('xml' or 'json')# If you pass a list, you will get a list of analyses# with the same structureanalyses=a.analyze_words([['А'],['Мон','тонь','вечктян','.']],format='xml')analyses=a.analyze_words(['Морфологиянть',[['А'],['Мон','тонь','вечктян','.']]],format='json')Refer to theuniparser-morph documentationfor the full list of options.DisambiguationDisambiguation is not yet available for this language.Word listsAlternatively, you can use a preprocessed word list. Thewordlistsdirectory contains a list of words from a 2.3-million-wordErzya corpus(wordlist_main.csv), list of analyzed tokens (wordlist_analyzed.txt; each line contains all possible analyses for one word in an XML format), and list of tokens the parser could not analyze (wordlist_unanalyzed.txt). The recall of the analyzer is 93.6% on literary texts and 90.7% on social media texts.Description formatThe description is carried out in theuniparser-morphformat and involves a description of the inflection (paradigms.txt), a grammatical dictionary (kpv_lexemes_XXX.txt files), a list of rules that annotate combinations of lexemes and grammatical values with additional Russian translations (lex_rules.txt), and a short list of analyses that should be avoided (bad_analyses.txt). The dictionary contains descriptions of individual lexemes, each of which is accompanied by information about its stem, its part-of-speech tag and some other grammatical/borrowing information, its inflectional type (paradigm), and Russian translation. See more about the formatin the uniparser-morph documentation.
Package: uniparser-komi-zyrian
Komi-Zyrian morphological analyzerThis is a rule-based morphological analyzer for Komi-Zyrian literary standard (kpv) of the Komi language continuum (Uralic > Permic). It is based on a formalized description of literary Komi-Zyrian morphology, which also includes a number of dialectal elements, and usesuniparser-morphfor parsing. It performs full morphological analysis of Komi-Zyrian words (lemmatization, POS tagging, grammatical tagging, glossing).How to usePython packageThe analyzer is available as a Python package. If you want to analyze Komi texts in Python, install the module:pip3 install uniparser-komi-zyrianImport the module and create an instance ofKomiZyrianAnalyzerclass. Setmode='strict'if you are going to process text in standard orthography, ormode='nodiacritics'if you expect some words to lack the diacritics (which often happens in social media). After that, you can either parse tokens or lists of tokens withanalyze_words(), or parse a frequency list withanalyze_wordlist(). Here is a simple example:fromuniparser_komi_zyrianimportKomiZyrianAnalyzera=KomiZyrianAnalyzer(mode='strict')analyses=a.analyze_words('Морфологияса')# The parser is initialized before first use, so expect# some delay here (usually several seconds)# You will get a list of Wordform objects# The analysis attributes are stored in its properties# as string values, e.g.:foranainanalyses:print(ana.wf,ana.lemma,ana.gramm,ana.gloss)# You can also pass lists (even nested lists) and specify# output format ('xml' or 'json')# If you pass a list, you will get a list of analyses# with the same structureanalyses=a.analyze_words([['А'],['Ме','тэнӧ','радейта','.']],format='xml')analyses=a.analyze_words(['Морфологияса',[['А'],['Ме','тэнӧ','радейта','.']]],format='json')Refer to theuniparser-morph documentationfor the full list of options.DisambiguationDisambiguation is not yet available for this language.Word listsAlternatively, you can use a preprocessed word list. Thewordlistsdirectory contains a list of words from a 1.8-million-wordKomi-Zyrian corpus(wordlist_main.csv), list of analyzed tokens (wordlist_analyzed.txt; each line contains all possible analyses for one word in an XML format), and list of tokens the parser could not analyze (wordlist_unanalyzed.txt). The recall of the analyzer is 92.2% on literary texts and 89.1% on social media texts.Description formatThe description is carried out in theuniparser-morphformat and involves a description of the inflection (paradigms.txt), a grammatical dictionary (kpv_lexemes_XXX.txt files), a list of rules that annotate combinations of lexemes and grammatical values with additional Russian translations (lex_rules.txt), and a short list of analyses that should be avoided (bad_analyses.txt). The dictionary contains descriptions of individual lexemes, each of which is accompanied by information about its stem, its part-of-speech tag and some other grammatical/borrowing information, its inflectional type (paradigm), and Russian translation. See more about the formatin the uniparser-morph documentation.
Package: uniparser-moksha
Moksha morphological analyzerThis is a rule-based morphological analyzer for Moksha (mdf; Uralic > Mordvinic). It is based on a formalized description of literary Moksha morphology, which also includes a number of dialectal elements, and usesuniparser-morphfor parsing. It performs full morphological analysis of Moksha words (lemmatization, POS tagging, grammatical tagging, glossing).How to usePython packageThe analyzer is available as a Python package. If you want to analyze Moksha texts in Python, install the module:pip3 install uniparser-mokshaImport the module and create an instance ofMokshaAnalyzerclass. Setmode='strict'if you are going to process text in standard orthography, ormode='nodiacritics'if you expect some words to lack the diacritics (which often happens in social media). After that, you can either parse tokens or lists of tokens withanalyze_words(), or parse a frequency list withanalyze_wordlist(). Here is a simple example:fromuniparser_mokshaimportMokshaAnalyzera=MokshaAnalyzer(mode='strict')analyses=a.analyze_words('Морфологиять')# The parser is initialized before first use, so expect# some delay here (usually several seconds)# You will get a list of Wordform objects# The analysis attributes are stored in its properties# as string values, e.g.:foranainanalyses:print(ana.wf,ana.lemma,ana.gramm,ana.gloss)# You can also pass lists (even nested lists) and specify# output format ('xml' or 'json')# If you pass a list, you will get a list of analyses# with the same structureanalyses=a.analyze_words([['А'],['Мон','тонь','келькте','.']],format='xml')analyses=a.analyze_words(['Морфологиять',[['А'],['Мон','тонь','келькте','.']]],format='json')Refer to theuniparser-morph documentationfor the full list of options.DisambiguationDisambiguation is not yet available for this language.Word listsAlternatively, you can use a preprocessed word list. Thewordlistsdirectory contains a list of words from a 1.7-million-wordMoksha corpus(wordlist_main.csv), list of analyzed tokens (wordlist_analyzed.txt; each line contains all possible analyses for one word in an XML format), and list of tokens the parser could not analyze (wordlist_unanalyzed.txt). The recall of the analyzer is 91% on literary texts and 79% on social media texts.Description formatThe description is carried out in theuniparser-morphformat and involves a description of the inflection (paradigms.txt), a grammatical dictionary (kpv_lexemes_XXX.txt files), a list of rules that annotate combinations of lexemes and grammatical values with additional Russian translations (lex_rules.txt), and a short list of analyses that should be avoided (bad_analyses.txt). The dictionary contains descriptions of individual lexemes, each of which is accompanied by information about its stem, its part-of-speech tag and some other grammatical/borrowing information, its inflectional type (paradigm), and Russian translation. See more about the formatin the uniparser-morph documentation.
Package: uniparser-ossetic
Ossetic (Iron) morphological analyzerThis is a rule-based morphological analyzer for Ossetic (oss). It is based on a formalized description of the morphology of literary Ossetic, which is based on the Iron dialect, and usesuniparser-morphfor parsing. It performs full morphological analysis of Ossetic words (lemmatization, POS tagging, grammatical tagging).How to usePython packageThe analyzer is available as a Python package. If you want to analyze Ossetic texts in Python, install the module:pip3 install uniparser-osseticImport the module and create an instance ofOsseticAnalyzerclass. Setmode='strict'if you are going to process text in standard orthography, ormode='nodiacritics'if you expect theӕcharacter to be misrepresented in some words (either as an identically looking Latin character or asае). After that, you can either parse tokens or lists of tokens withanalyze_words(), or parse a frequency list withanalyze_wordlist(). Here is a simple example:fromuniparser_osseticimportOsseticAnalyzera=OsseticAnalyzer(mode='strict')analyses=a.analyze_words('ӕвзаджы')# The parser is initialized before first use, so expect# some delay here (usually several seconds)# You will get a list of Wordform objects# The analysis attributes are stored in its properties# as string values, e.g.:foranainanalyses:print(ana.wf,ana.lemma,ana.gramm)# You can also pass lists (even nested lists) and specify# output format ('xml', 'json' or 'conll')# If you pass a list, you will get a list of analyses# with the same structureanalyses=a.analyze_words([['Фӕлӕ'],['Æз','дæ','уарзын','.']],format='xml')analyses=a.analyze_words([['Фӕлӕ'],['Æз','дæ','уарзын','.']],format='conll')analyses=a.analyze_words(['ӕвзаджы',[['Фӕлӕ'],['Æз','дæ','уарзын','.']]],format='json')Refer to theuniparser-morph documentationfor the full list of options.Word listsAlternatively, you can use a preprocessed word list. Thewordlistsdirectory contains a list of words from a 12-million-wordOssetic National Corpus(wordlist.csv) with 438,000 unique tokens, list of analyzed tokens (wordlist_analyzed.txt; each line contains all possible analyses for one word in an XML format), and list of tokens the parser could not analyze (wordlist_unanalyzed.txt). The recall of the analyzer on the corpus texts is about 90%.Description formatThe description is carried out in theuniparser-morphformat and involves a description of the inflection (oss_paradigms.txt), a grammatical dictionary (oss_lexemes.txt), a list of productive lemma-changing derivations (derivations.txt), and a short list of analyses that should be avoided (bad_analyses.txt). The dictionary contains descriptions of individual lexemes, each of which is accompanied by information about its stem, its part-of-speech tag and some other grammatical information, its inflectional type (paradigm), and Russian and/or English translation. See more about the formatin the uniparser-morph documentation.
Package: uniparser-urmi
Urmi morphological analyzerThis is a rule-based morphological analyzer for Christian Urmi (Afro-Asiatic > North-Eastern Neo-Aramaic). It is based on a formalized description of Urmi morphology and usesuniparser-morphfor parsing. It performs full morphological analysis of Urmi words (lemmatization, POS tagging, grammatical tagging). The text to be analyzed should be written in the Latin-based alphabet (the Assyrian New Alphabet).How to usePython packageThe analyzer is available as a Python package. If you want to analyze Urmi texts in Python, install the module:pip3 install uniparser-urmiImport the module and create an instance ofUrmiAnalyzerclass. Setmode='strict'if you are going to process text in standard Assyrian New Alphabet, ormode='nodiacritics'if you expect some words to lack the diacritics (e.g.tinstead ofṭ). After that, you can either parse tokens or lists of tokens withanalyze_words(), or parse a frequency list withanalyze_wordlist(). Here is a simple example:fromuniparser_urmiimportUrmiAnalyzera=UrmiAnalyzer(mode='strict')analyses=a.analyze_words('вajjannux')# The parser is initialized before first use, so expect# some delay here (usually several seconds)# You will get a list of Wordform objects# The analysis attributes are stored in its properties# as string values, e.g.:foranainanalyses:print(ana.wf,ana.lemma,ana.gramm)# You can also pass lists (even nested lists) and specify# output format ('xml', 'json' or 'conll')# If you pass a list, you will get a list of analyses# with the same structureanalyses=a.analyze_words([['вajjannux'],['ʕəbarwo','lab','bote','.']],format='xml')analyses=a.analyze_words([['вajjannux'],['ʕəbarwo','lab','bote','.']],format='conll')analyses=a.analyze_words(['вajjannux',[['laḥmawo'],['ʕəbarwo','lab','bote','.']]],format='json')Refer to theuniparser-morph documentationfor the full list of options.If you want to quickly check an analysis for one particular word, you can also use the command-line interface. Here is an example for the wordвajjannux:python3 -m uniparser_urmi вajjannuxWord listsAlternatively, you can use a preprocessed word list. Thewordlistsdirectory contains a list of words from a 622-thousand-wordChristian Urmi corpus(wordlist.csv) with 63,000 unique tokens, list of analyzed tokens (wordlist_analyzed.txt; each line contains all possible analyses for one word in an XML format), and list of tokens the parser could not analyze (wordlist_unanalyzed.txt). The recall of the analyzer on the corpus texts is about 76%.Description formatThe description is carried out in theuniparser-morphformat and involves a description of the inflection (paradigms.txt) and a grammatical dictionary (lexemes.txt). The dictionary contains descriptions of individual lexemes, each of which is accompanied by information about its stem, its part-of-speech tag and some other grammatical information, its consonant root, its inflectional type (paradigm), and English and/or Russian translations. See more about the formatin the uniparser-morph documentation.
Package: unipay
UniPay: 联合收款码封装（二合一收款码）🌟UniPay是一个简洁高效的工具，封装了微信和支付宝的联合收款码，使得收款更加方便和高效。🔍 项目介绍UniPay是一个针对微信和支付宝收款码进行封装的工具，旨在简化和统一收款流程，提供一种简洁的方式为你的业务接收付款。📋 功能特性支持微信和支付宝收款码。支持自动跳转到收款码页面，PC 端展示双端收款码图片。支持自定义收款码 ID。支持自定义收款码样式。📦 安装通过pip进行安装:sudoaptinstalllibzbar-dev
pipinstallunipay🚀 使用方法启动UniPay:unipay访问http://127.0.0.1:8600打开首页访问http://127.0.0.1:8600/docs查看 API 文档。使用create_unipay接口生成联合收款码。访问http://127.0.0.1:8600/xxxxxxxx查看收款码。🖼️预览:⚠️ 注意事项目前项目正在开发中，暂未提供任何配置。不建议在生产环境中使用。
Package: unipay-sdk
unipay-sdkPython SDK for unipay.com payments gatewayInstallpip3installunipay-sdkHow to ...Create orderfromunipay_sdkimportUniPayClientfromunipay_sdkimportOrderItemclient=UniPayClient(secretkey='mySecretKeyFromUniPay',merchant_id=00000000000000,success_url='/url/success',cancel_url='/url/cancel',callback_url='/url/callback')new_order=client.create_order(merchant_order_id='ORDER1234',# Unique order idmerchant_order_user='1',# User id on merchant sideorder_price=15.35,# in GELorder_name='Purchase on example.com',order_description='Some description',items=[OrderItem.from_dict({'price':7.0,'quantity':1,'title':'Coffee','description':'Cup of coffee'}),OrderItem.from_dict({'price':8.35,'quantity':1,'title':'Cake','description':'Big cake'})])checkout_link=new_order.data.Checkoutorder_hash=new_order.data.UnipayOrderHashIDHandle errorsfromunipay_sdkimportUniPayClientfromunipay_sdkimportOrderItemfromunipay_sdkimportAPIErrorclient=UniPayClient(...)new_order=client.create_order(...)matchnew_order.errorcode:caseAPIError.OK:print('OK')caseAPIError.FREQUENCY_LIMIT_EXCEEDED:print('Rate limited')# etc...Test serverTo test order workflows you may useunipay-test-serverTo use test server you might initializeUniPayClientwithtestflagfromunipay_sdkimportUniPayClienttest_client=UniPayClient(...,test=True,test_url='http://localhost:8080')# test_url is path to unipay-test-serverValidateUniPay-SDK also provides Pydantic 2.0 schemas for callbacks and responsesfromunipay_sdk.pydanticimportBasicUniPayCallbackfromunipay_sdk.pydanticimportBasicUniPayResponseFound error or want to contribute?You may create a pull request to repository
Package: unipdgest.portlet.navigation
This is a simplePlone navigation portletthat provide anaccordionbehavior
on top levelsHow it worksThe new portlet is an extension of the default Plone navigation but some of it’s features has been removed:Starting levelShow top levelDisplay current folderAccordion navigation will show the choosen root folder and offer the accordion only on higher level of the tree.CreditsDeveloped with the support ofUniversity of Padova - Department of Management and Engineering;
University of Padova supports thePloneGov initiative.AuthorsThis product was developed by RedTurtle Technology team.Changelog0.1.0 (2014-07-25)Initial release
Package: unipercept
UniPerceptInstallationThis package requires at least Python 3.11 and PyTorch 2.1. Once you have created an environment with these
dependencies, we can proceed to installuniperceptusing one of three installation methods.Stable release (recommended)You can install the latest stable release from PyPI viapipinstalluniperceptMaster branchTo install the latest version, which is not guaranteed to be stable, install from GitHub usingpipinstallgit+https://github.com/kurt-stolle/unipercept.gitDevelopersIf your use-case requires changes to our codebase, we recommend that you first fork this repository and download your
own fork locally. Assuming you have the GitHub CLI installed, you can clone your fork withghrepocloneuniperceptThen, you can proceed to install the package in editable mode by runningpipinstall--editableuniperceptYou are invited to share your improvements to the codebase through a pull request on this repository.
Before making a pull request, please ensure your changes follow our code guidelines by runningpre-commitbefore
adding your files into a Git commit.Training and evaluationModels can be trained and evalurated from the CLI or through the Python API.CLITo train a model with the CLI:uniclitrain--config<configpath>Without a<config name>, an interactive prompt will be started to assist in finding a configuration file.Developer guidelinesAll tests can ran viapython -m pytest.
However, we also provide amakedirective that usespytorch-xdistto speed up the process:make testYou may need to tune the parameters if memory problems arise during testing.Similarly, benchmarks are implemented usingpytest-benchmark. To run them, use:make benchmarkCoverage reports are built usingpytest-cov, and executed using:make coverageLast, we largely follow the same principles and methods asTransformersuses for testing.
For more information on usingpytestfor automated testing, check outtheir documentation.AcknowledgementsWe would like to express our gratitude to the developers of the following open-source projects, which have significantly contributed to the success of our work:PyTorch: An open-source machine learning framework that accelerates the path from research prototyping to production deployment.Detectron2: A platform for object detection and segmentation built on PyTorch. We liberally use the packages and code from this project.PyTorch3D: A library on which we base our camera projection from 2D to 3D space.Panoptic FCN: An implementation of the Panoptic FCN method for panoptic segmentation tasks.ViP-DeepLab: The baseline implementation for the depth-aware video panoptic segmentation task.Panoptic Depth: A repository that implements the instance (de)normalization procedure that significantly improves depth estimation forthings.The Unified Perception implementation contains extracts of the above repositories that have been edited to suit the specific needs of this project.
Whenever possible, the original libraries are used instead.LicenseThis repository is released under the MIT License. For more information, please refer to the LICENSE file.