Saravanan666/pypi-org huggingface dataset:
Package: faststack
FaststackFaststack is a batteries-included framework for rapidly building web apps on FastAPI.Batteries includedFaststack provides an opinionated, but out of the box configuration for:HTTP framework:FastAPIWeb server:HypercornDependency injection:KinkORM:SQLModelMigrations:AlembicCLI:TyperUser management: ...Admin:FastAPI AdminTyping & Async first-class by defaultFlexibility for small and large apps alikeFaststack is designed to support single-file web scripts as well as onion-architecture
monoliths. The structure of your app is largely up to you, but Faststack provides a
sensible default structure that can be used as a starting point for most apps.
Package: fast-stats
fast-statsfast-statsis a fast and simple library for calculating basic statistics such as: precision, recall, and f1-score. The library also supports the calculation of confusion matrices. For examples, please look at theexamples/folder.The project was developed using thematurinframework.See docs here:https://zachcoleman.github.io/fast-stats/InstallationFrom PyPI:pipinstallfast-statsBuild from source:maturin build -r -i=path/to/python
pip install .../fast-stats/target/wheels/<whl file name>.whlRunning TestsTests are run withpytest.
Package: faststyle
faststyleFaststyle aims to provide an easy and modular interface to Image to Image problems based onfeature loss.InstallMaking sure you have a working installation offastai. You can then install the library by doing:pip install faststyleYou can instead use a editable install, which is more recommended since the lib is chaging a lot:git clone https://github.com/lgvaz/faststyle.git
cd faststyle
pip install -e .ContributingThere are still tons of things to do in the library, you're welcome to contribute. Some of the tasks are listed in the issues, feel free to contact me directly for more info =)  .The library is built withnbdev, I'm following the coding style used by fastai (the only difference being that I use 2 spaces for indentation instead of 4), take a look at the cotribution guidelinehere.
Package: fasttask
Trabalho Prático Engenharia de Software 2Grupo:Francisco Bonome AndradeLucas Caetano Lopes RodriguesLucas Starling de Paula SallesYasmin AraújoFastTaskO FastTask é um sistema para gestão de tarefas. O sistema é capaz de gerenciar diferentesboards, nos quais o usuário pode criar tarefas, com nomes, descrições, e prioridade.Para executar o sistema, instale-o através do pip:pip install fasttaskO usuário pode interagir com o sistema utilizando a CLI:$fasttaskboardcreateUFMG--labelPessoal
$fasttasktasklist
OnDefault(id=1):

$fasttaskboardlist
Default(id=1)UFMG(id=2)$fasttaskboardcheckout2$fasttasktaskcreateprojeto_es2_entrega_1--label"ES2"$fasttasktasklist
OnUFMG(id=2):
projeto_es2_entrega_1

$fasttasktaskdescribeprojeto_es2_entrega_1
Taskdetails:
Id:1Name:projeto_es2_entrega_1
Status:ToDo
Creationdate:29/05/2022
Label:ES2
Priority:0TimeWorkedinthistask:0Tecnologias utilizadasO sistema foi desenvolvido utilizando Python 3.7+ para construção da CLI e das classes principais do sistema, e SQLite para armazenar osboardse as tarefas criadas. A ferramenta Unittest também foi utilizada para criar testes unitários para os componentes do sistema.Relatórios do LizardPara gerar os relatórios, utilizamos a seguinte configuração para o Lizard:Threshold paracyclomatic_complexityé 5Ordenar por: 1) cyclomatic_complexity; 2) lengthPara os arquivos do sistema emsrc/Você pode encontrar todos os relatórios na pastalizard_reports.O comando utilizado foi:$lizard-Tcyclomatic_complexity=5-scyclomatic_complexity-slengthpathssrc/main.pysrc/modules/dbhandler.pysrc/modules/clicontroller.pysrc/modules/command.pysrc/modules/board.pysrc/modules/task.pyApós finalizar o projeto inicial do sistema, esses eram os warnings gerados:==========================================================================================!!!!Warnings(cyclomatic_complexity>5orlength>1000ornloc>1000000orparameter_count>100)!!!!================================================NLOCCCNtokenPARAMlengthlocation------------------------------------------------247145124parse@82-105@src/modules/command.py16793116shell@110-125@src/modules/command.py176107117parse_flags@136-152@src/modules/command.py==========================================================================================TotalnlocAvg.NLOCAvgCCNAvg.tokenFunCntWarningcntFunRtnlocRt
------------------------------------------------------------------------------------------4916.61.543.27230.040.12Com as refatorações, as três funções mais complexas pararam de gerar warnings:==========================================================================================Nothresholdsexceeded(cyclomatic_complexity>5orlength>1000ornloc>1000000orparameter_count>100)==========================================================================================TotalnlocAvg.NLOCAvgCCNAvg.tokenFunCntWarningcntFunRtnlocRt
------------------------------------------------------------------------------------------5186.61.542.67600.000.00
Package: fasttask-manager
manager forfasttaskcreate projectpython-mfasttask_manager.create_projectcreate a Managerm=Manager("127.0.0.1",task_name="get_hypotenuse",port=8080)run a taskresult=m.run(params)create a task and check result laterresult_id=m.create_task(params)# do something...# do something...# do something...result=m.check(result_id)create a task and wait for resultresult=m.create_and_wait_result(params)
Package: fast-tcrdist
fast_tcrdistfast_tcrdist is an optimized version of the TCRDist algorithm published by Dash et al. Nature (2017): doi:10.1038/nature22383To enhance the original implementation of TCRDist, fast_tcrdist uses the Needleman-Wunsch alignment algorithm to align TCR sequences
and creates the TCRDist matrix via cython.  To integrate well with other common single cell analysis tools, fast_tcrdist utilizes
the Anndata data structure to store the TCRDist matrix and associated metadata.  Currently, this has been tested on TCR/gene expression
output from Cellranger (https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger), but
future releases will aim to allow for other file formats.In addition to running the TCRDist algorithm, fast_tcrdist allows you to aggregate TCR info with single-cell gene expression into a single
anndata object to allow for integrated downstream analyses.Outside filesThe BLOSUM62 matrix used for alignments came from NCBI:https://www.ncbi.nlm.nih.gov/IEB/ToolBox/C_DOC/lxr/source/data/BLOSUM62CDR amino acid info was taken from the TCRDist database file "alphabeta_db.tsv" (https://www.dropbox.com/s/kivfp27gbz2m2st/tcrdist_extras_v2.tgz)
and reformated into .json format (mouse_CDRs_for_10X.json and human_CDRs_for_10X.json)
Package: fasttest
fasttest在macaca、appium、selenium的基础上做了一层关键字映射，在yaml文件上编写自动化用例，即使无代码基础的同学也已可以很快上手自动化测试我能做什么支持IDEA、Pycharm插件，在yaml文件上写用例可智能联想关键字 -->FastYaml支持实时debug用例步骤，无需重复运行验证支持现有关键字组合、自定义关键字，拥有无限扩展性支持PO模式、支持iOS、Android两端共用一份用例支持if、while、for等语法用于构造复杂场景支持CLI命令，支持Jenkins持续集成支持多设备并行执行演示↓↓↓更多请点击fasttest
Package: fast-test-database
Use a pure in-memory database for running Django tests.UsageInsettings.py:fromfast_test_databaseimportfast_test_databaseDATABASES=fast_test_database(DATABASES)# Or:DATABASES=fast_test_database(DATABASES,test_commands=('test','harvest'))# Or:DATABASES=fast_test_database(DATABASES,version='5.7')This will be a no-op except for./manage.py test, when an in-memory
database will be automatically started and supplied to the application.DetailsThe in-memory database is a full PostgreSQL or MySQL instance started
using Docker, using tmpfs for storing the data. A single container will
be started if not yet running. It will not be shut down automatically,
and instead reused for subsequent tests.The type of the database (PostgreSQL or MySQL) is chosen based on the
existing default database engine.The default version of the database (PostgreSQL or MySQL) is latest.
But it can be specified by version parameter.
Package: fasttextannotator
Annotates corpuses for use in FastText over a Command Line Interface.Raison D'être :thought_balloon:This allows a large corpus to be annotated easily using a command line interface for purposes of being used in aFastTextsupervised classifier.Architecture :triangular_ruler:fasttext-annotatorhas a very simple architecture that basically reads each line of the file fed to it, and asks the user for an annotation given the options provided to it via the command line.Dependencies :globe_with_meridians:promptInstallation :inbox_tray:You can install this via the PyPi package manager, or manually.ManuallyRunpython setup.py --installVia PypiRunpip install fasttextannotatorUsage example :eyes:In order to annotate a corpus of text, execute the following:fasttextannotator[corpus-file][output-file][labels...]You will then be presented with a line of text from the corpus, and a range of options based on the labels you provided.License :memo:The project is available under theMITlicense.AcknowledgementsIcon in README banner istextby Chameleon Design from the Noun Project.
Package: fasttext-github
fastTextis a library for efficient learning
of word representations and sentence classification.RequirementsfastTextbuilds on modern Mac OS and Linux
distributions. Since it uses C++11 features, it requires a compiler with
good C++11 support. These include :(gcc-4.8 or newer) or (clang-3.3 or newer)You will needPythonversion 2.7 or >=3.4NumPy&SciPypybind11Building fastTextThe easiest way to get the latest version offastText is to use
pip.$ pip install fasttextIf you want to use the latest unstable release you will need to build
from source using setup.py.Now you can import this library withimport fastTextExamplesIn general it is assumed that the reader already has good knowledge of
fastText. For this consider the mainREADMEand in particularthe tutorials on our
website.We recommend you look at theexamples within the doc
folder.As with any package you can get help on any Python function using the
help function.For example+>>> import fastText
+>>> help(fastText.FastText)

Help on module fastText.FastText in fastText:

NAME
    fastText.FastText

DESCRIPTION
    # Copyright (c) 2017-present, Facebook, Inc.
    # All rights reserved.
    #
    # This source code is licensed under the MIT license found in the
    # LICENSE file in the root directory of this source tree.

FUNCTIONS
    load_model(path)
        Load a model given a filepath and return a model object.

    tokenize(text)
        Given a string of text, tokenize it and return a list of tokens
[...]IMPORTANT: Preprocessing data / enconding conventionsIn general it is important to properly preprocess your data. In
particular our example scripts in theroot
folderdo this.fastText assumes UTF-8 encoded text. All text must beunicode for
Python2andstr for
Python3.
The passed text will beencoded as UTF-8 by
pybind11before passed to the fastText C++ library. This means it is important to
use UTF-8 encoded text when building a model. On Unix-like systems you
can convert text usingiconv.fastText will tokenize (split text into pieces) based on the following
ASCII characters (bytes). In particular, it is not aware of UTF-8
whitespace. We advice the user to convert UTF-8 whitespace / word
boundaries into one of the following symbols as appropiate.spacetabvertical tabcarriage returnformfeedthe null characterThe newline character is used to delimit lines of text. In particular,
the EOS token is appended to a line of text if a newline character is
encountered. The only exception is if the number of tokens exceeds the
MAX_LINE_SIZE constant as defined in theDictionary
header.
This means if you have text that is not separate by newlines, such as
thefil9 dataset, it will be
broken into chunks with MAX_LINE_SIZE of tokens and the EOS token is
not appended.The length of a token is the number of UTF-8 characters by considering
theleading two bits of a
byteto identifysubsequent bytes of a multi-byte
sequence.
Knowing this is especially important when choosing the minimum and
maximum length of subwords. Further, the EOS token (as specified in theDictionary
header)
is considered a character and will not be broken into subwords.
Package: fasttext-language-detection
fasttext-language-detectionInstallpipinstallfasttext-language-detectionUsagefromlangdetection.detectorimportLanguageDetectorlanguage_detector=LanguageDetector(low_memory=True)text='Hello world!'detection=language_detector.detect(text)print(detection)>>{'lang':'en','prob':0.22845280170440674}Testsmakerun_testsList of supported languagesaf als am an ar arz as ast av az azb ba bar bcl be bg bh bn bo bpy br bs bxr ca cbk ce ceb ckb co cs cv cy da de diq dsb dty dv el eml en eo es et eu fa fi fr frr fy ga gd gl gn gom gu gv he hi hif hr hsb ht hu hy ia id ie ilo io is it ja jbo jv ka kk km kn ko krc ku kv kw ky la lb lez li lmo lo lrc lt lv mai mg mhr min mk ml mn mr mrj ms mt mwl my myv mzn nah nap nds ne new nl nn no oc or os pa pam pfl pl pms pnb ps pt qu rm ro ru rue sa sah sc scn sco sd sh si sk sl so sq sr su sv sw ta te tg th tk tl tr tt tyv ug uk ur uz vec vep vi vls vo wa war wuu xal xmf yi yo yue zhLicenseThefastetext modelsare distributed under theCreative Commons Attribution-Share-Alike License 3.0.References[1] A. Joulin, E. Grave, P. Bojanowski, T. Mikolov, Bag of Tricks for Efficient Text Classification@article{joulin2016bag,
  title={Bag of Tricks for Efficient Text Classification},
  author={Joulin, Armand and Grave, Edouard and Bojanowski, Piotr and Mikolov, Tomas},
  journal={arXiv preprint arXiv:1607.01759},
  year={2016}
}[2] A. Joulin, E. Grave, P. Bojanowski, M. Douze, H. Jégou, T. Mikolov, FastText.zip: Compressing text classification models@article{joulin2016fasttext,
  title={FastText.zip: Compressing text classification models},
  author={Joulin, Armand and Grave, Edouard and Bojanowski, Piotr and Douze, Matthijs and J{\'e}gou, H{\'e}rve and Mikolov, Tomas},
  journal={arXiv preprint arXiv:1612.03651},
  year={2016}
}
Package: fasttext-parallel
Multithreading For FastTextAn easy to use tool for multithreading fasttext batch inference.FeaturesFast inference using multithreading utilizing all cpu cores.Predicted labels are encoded as i16 numpy array for small memory footprint and easy serialization.PerformanceOn macbook Air M2 chip, the performance is about 4 times faster,
which is reasonable since it has 4 performance cores.❯ python -m unittest discover test
fasttext-parallel 1.7130832079565153s
fasttext          6.7440170829650015sUsagepip install fasttext-parallelimportfasttext_parallelasftmodel=ft.load_model("./model/lid.176.bin")# this uses multiple threadslabels,probabilities=model.batch(["你好","how are you"])# labels are in a format of numpy.ndarray (i16) format# to get actual label, call get_label_by_idassertmodel.get_label_by_id(labels[0][0])=="__label__zh"assertmodel.get_label_by_id(labels[1][0])=="__label__en"# to view all labels (a dict from label_id to label)print(model.get_labels())
Package: fasttext-predict
Python package forfasttext:keep only thepredictmethod, all other features are removedstandalone package without external dependency (numpy is not a
dependency)wheels for various architectures using GitHub workflows. The script
is inspired by lxml build scripts.Usagepipinstallfasttext-predictimportfasttextmodel=fasttext.load_model('lid.176.ftz')result=model.predict('Fondant au chocolat et tarte aux myrtilles')Seehttps://fasttext.cc/docs/en/language-identification.html
Package: fasttextsearch
InstallationWith pippip install fasttextsearchFor developerspipinstallnumpy

gitclonehttps://github.com/danpovey/text_searchcdtext_search

mkdirbuildcdbuild
cmake..
make-j
maketest# set PYTHONPATH so that you can use "import textsearch"exportPYTHONPATH=$PWD/../textsearch/python:$PWD/lib:$PYTHONPATHNow you can usepython3-c"import textsearch; print(textsearch.__file__)"Caution: We did not use eitherpython3 setup.py installorpip install.
We only set the environment variablePYTHONPATH.
Package: fasttext-serving-server
fasttext-servingfastText model serving serviceInstallationYou can download prebuilt binary fromGitHub releases,
or install it using Cargo:cargoinstallfasttext-servingUsing Docker:dockerpullmessense/fasttext-servingUsage$fasttext-serving--help

USAGE:fasttext-serving[OPTIONS]--model<model>

FLAGS:--grpcServinggRPCAPIinsteadofHTTPAPI-h,--helpPrintshelpinformation-V,--versionPrintsversioninformation

OPTIONS:-a,--address<address>Listenaddress[default:127.0.0.1]-m,--model<model>Modelpath-p,--port<port>Listenport[default:8000]-w,--workers<workers>Workerthreadcount,defaultstoCPUcountServe HTTP REST APIHTTP API endpoint:POST /predictPost data should be JSON array of string, for example["abc", "def"]CURL example:$curl-XPOST-H'Content-Type: application/json'\--data"[\"Which baking dish is best to bake a banana bread?\", \"Why not put knives in the dishwasher?\"]"\'http://localhost:8000/predict'[[["baking"],[0.7152988]],[["equipment"],[0.73479545]]]Serve gRPC APIRun the command with--grpcto serve gRPC API instead of HTTP REST API.Please refer to gRPC Python client documentationhere.LicenseThis work is released under the MIT license. A copy of the license is provided in theLICENSEfile.
Package: fast-tmfg
Fast-TMFGFast_TMFG is an ultra-fast implementation of the Triangulated Maximally Fileterd Graph (TMFG). It is based on the work byGuido Previde Massaraand is fully implemented byAntonio BriolaandTong Zheng.The interface is fully scikit-learn compatible. Consequently, it has three main methods:fit(weights, cov, output): Fits the model to the input matrixweights(e.g. a squared correlation matrix) and input matrixcov(e.g. covariance matrix). This method computes the Triangulated Maximal Filtered Graph (TMFG) based on the input weight matrix. Theoutputparameter specifies what is the nature of the desired output:sparse inverse covariance matrix (output = 'logo')sparse unweighted weights matrix (output = 'unweighted_sparse_W_matrix')sparse weighted weights matrix (output = 'weighted_sparse_W_matrix')transform(): Returns the computed cliques and separators set of the model. The method also returns the TMFG adjacency matrix.fit_transform(weights, cov, output): Fits the model to the input matrixweights(e.g. a squared correlation matrix) and input matrixcov(e.g. covariance matrix), and returns the computed cliques and separators set and the TMFG adjacency matrix over the covariance matrix input. Theoutputparameter specifies what is the nature of the desired output:sparse inverse covariance matrix (output = 'logo')sparse unweighted weights matrix (output = 'unweighted_sparse_W_matrix')sparse weighted weights matrix (output = 'weighted_sparse_W_matrix')We provide a detailed explanation of each function/method. Such an explanation is entirely generated throughChatGPT.For a full understanding of the TMFG, we refer the interested reader to the following papers:Parsimonious modelling with information filtering networksNetwork filtering for big data: Triangulated maximally filtered graphDependency structures in cryptocurrency market from high to low frequencyInstallationInstall the latest version of the package usingPyPI:pip3 install fast-tmfgUsage Exampleimportnumpyasnpimportpandasaspdfromfast_tmfgimport*defgenerate_random_df(num_rows,num_columns):data=np.random.randint(0,100,size=(num_rows,num_columns))df=pd.DataFrame(data,columns=['col_{}'.format(i)foriinrange(num_columns)])returndfdf=generate_random_df(100,50)corr=np.square(df.corr())cov=df.cov()model=TMFG()cliques,seps,adj_matrix=model.fit_transform(corr,cov,output='unweighted_sparse_W_matrix')How to cite usIf you use TMFG in a scientific publication, we would appreciate citations to the following paper:@article{briola2022dependency,
  title={Dependency structures in cryptocurrency market from high to low frequency},
  author={Briola, Antonio and Aste, Tomaso},
  journal={arXiv preprint arXiv:2206.03386},
  year={2022}
}
Package: fast-token-classifier
INFO-EXTRACTIONNLP project to identify and categorize named entities in an input text.Table of ContentINFO-EXTRACTIONTable of ContentSetup PackageIMPORTANT STEPList Available CommandsBuild And Publish The PackageUsing SetuptoolsUsing PoetryCheck HugingFace CacheRun TestsSetup PackageIMPORTANT STEPTo setup the package locally, run:makesetup_venvList Available CommandsTo list all the available commands, run:makehelpBuild And Publish The PackageUsing SetuptoolsBuild the package by running:# Install packages required for building and publishingpython-mpipinstallbuildtwine# Buildpythonsetup.pyclean--all
pythonsetup.pysdistbdist_wheel# Verify buildtwinecheckdist/*# Upload packagetwineuploaddist/*--verboseUsing PoetryBuild the package using Poetry by running:# Install packages required for building and publishingpipinstall--no-cachepoetry==1.4.2
poetrylock"--no-update"&&poetryinstall--no-interactionexportPYPI_TOKEN="your_pypi_token"poetryconfigpypi-token.pypi${PYPI_TOKEN}# Build and upload packagepoetrypublish--buildCheck HugingFace CacheCheck the locally cached models and dataset by running:huggingface-cliscan-cache-vRun TestsFor unit tests, run:# All testsmakerun_test
Package: fasttokenizer
FastTokenizerFastTokenizeris a tokenizer meant to perform language agnostic
tokenization using unicode information.While the initial goal is to design a tokenizer for the purpose of
machine translation, the same tokenizer is generic enough to be adapted
to a wide range of tasks in NLP due to its' ability to handle a wide
range of languages and writing systems.Some of the notable features ofFastTokenizerareProvidingjustthe right amount of tokenization.Segmentation are designed to be intuitive and rule based.
The format is ideal for downstream NLP models like subword modelling,
RNNs or transformers.Also designed to be not so aggressive.
This way number of tokens can be kept down, allowing model to run faster.Works for any and every langauge/writing system.Cross programming language.Performs format retaining unicode normalization.Performance matches or exceeds moses-tokenizer on tasks such as WMT and GLUE.Tokenization can be reversed.However custom desegmenter should be used to achieve desired formatting
as desegmentation is highly use-case driven.Comparison with other tokenizers from the webSource:          他的表现遭到《天空体育》评论员内维尔的批评。
Segmenter:       ['他的表现遭到', '《', '天空体育', '》', '评论员内维尔的批评', '。']
Moses:           ['他的表现遭到《天空体育》评论员内维尔的批评。']
Spacy Tokenizer: ['他的表现遭到《天空体育》评论员内维尔的批评。']
Tweet Tokenizer: ['他的表现遭到', '《', '天空体育', '》', '评论员内维尔的批评', '。']
NLTK Tokenizer:  ['他的表现遭到《天空体育》评论员内维尔的批评。'

Source:          AirPods耳機套
Segmenter:       ['AirPods', '耳機套']
Moses:           ['AirPods耳機套']
Spacy Tokenizer: ['AirPods耳機套']
Tweet Tokenizer: ['AirPods耳機套']
NLTK Tokenizer:  ['AirPods耳機套']

Source:          A typical master's programme has a duration of 1-1.5 years.
Segmenter:       ['A', 'typical', "master's", 'programme', 'has', 'a', 'duration', 'of', '1', '@-@', '1.5', 'years', '.']
Moses:           ['A', 'typical', 'master', "'s", 'programme', 'has', 'a', 'duration', 'of', '1', '@-@', '1.5', 'years', '.']
Spacy Tokenizer: ['A', 'typical', "master's", 'programme', 'has', 'a', 'duration', 'of', '1-1.5', 'years.']
Tweet Tokenizer: ['A', 'typical', "master's", 'programme', 'has', 'a', 'duration', 'of', '1-1', '.', '5', 'years', '.']
NLTK Tokenizer:  ['A', 'typical', 'master', "'s", 'programme', 'has', 'a', 'duration', 'of', '1-1.5', 'years', '.']InstallationC++Comming soon.PythonpipinstallfasttokenizerUsageC++#include<fasttokenizer/segmenter.h>Segmentersegmenter=Segmenter(args.protected_dash_split);std::stringtext="Hello World!";std::stringoutput;// Normalizeoutput=segmenter.normalize(text)// Segmentoutput=segmenter.segment(text)// Normalize and segment in one function// Reduce string to icu::UnicodeString overheadoutput=segmenter.normalize_and_segment(text);// Desegmentoutput=segmenter.desegment(text);Pythonimportfasttokenizersegmenter=fasttokenizer.Segmenter()text="Hello World!"# Normalizeoutput:str=segmenter.normalize(text)# Segmentoutput:str=segmenter.segment()# Normalize and segmentoutput:str=segmenter.normalize_and_segment(text)# Output of segment is str.# To get tokens, you can split by whitespace.tokens=output.split()# Desegmentoutput:str=segmenter.desegment(text)