Saravanan666/pypi-org huggingface dataset:
Package: transformertopic
transformertopicTopic Modeling using sentence embeddings. This procedure works very well: in practice it almost always produces sensible topics and (from a practical point of view) renders all LDA variants obsolete.This is my own implementation of the procedure describedherebyMaarten Grootendorst, who also has his own implementation availablehere. Thanks for this brilliant idea!I wanted to code it myself and have features marked with a ⭐, which as far as I know are not available in Grootendorst's implementation.Features:Compute topic modelingCompute dynamic topic modeling ("trends" here)⭐ Assign topics on sentence rather than document level⭐ Experiment with different dimension reducers⭐ Experiment with different ways to generate a wordcloud from a topic⭐ Infer topics of new batches of docs without retrainingHow it worksIn the following the words "cluster" and "topic" are used interchangeably. Please note that in classic Topic Modeling procedures (e.g. those based onLDA) each document is a probability distribution over topics. In this sense the procedure here presented could be considered as a special case where these distributions are always degenerate and concentrate the probability on one single index.The procedure is:split paragraphs into sentencescompute sentence embeddings (usingsentence transformers)compute dimension reduction of these embeddings (withumap,pacmap,tsneorpca)cluster them withHDBSCANfor each topic compute a "cluster representator": a dictionary with words as keys and ranks as values (usingtfidf,textrankorkmaxoids[^1])use the cluster representators to computewordclouds for each topic[^1]: my own implementation, seekmaxoids.pyInstallationpip install -U transformertopicUsageView alsotest.py.Choose a reducerfrom transformertopic.dimensionReducers import PacmapEmbeddings, UmapEmbeddings, TsneEmbeddings
#reducer = PacmapEmbeddings()
#reducer = TsneEmbeddings()
reducer = UmapEmbeddings(umapNNeighbors=13)Init and run the modelfrom transformertopic import TransformerTopic
tt = TransformerTopic(dimensionReducer=reducer, hdbscanMinClusterSize=20)
tt.train(documentsDataFrame=pandasDf, dateColumn='date', textColumn='coref_text', copyOtherColumns = True)
print(f"Found {tt.nTopics} topics")
print(tt.df.info())If you want to use different embeddings, you can pass the SentenceTransformer model name via thestEmbeddingsinit argument toTransformerTopic.Show sizes of largest topicsN = 10
topNtopics = tt.showTopicSizes(N)Choose a cluster representator and show wordclouds for the biggest topicsfrom transformertopic.clusterRepresentators import TextRank, Tfidf, KMaxoids
representator = Tfidf()
# representator = TextRank()
tt.showWordclouds(topNtopics clusterRepresentator=representator)Show frequency of topics over times (dynamic topic modeling), or trends:tt.showTopicTrends()Show topics in which "car" appears in the top 75 words in their cluster representation:tt.searchForWordInTopics("car", topNWords=75)
Package: transformer-vae
Transformer-VAE (WIP)Transformer-VAE's learn smooth latent spaces of discrete sequences without any explicit rules in their decoders.This can be used for program synthesis, drug discovery, music generation and much more!To see how it works checkoutthis blog post.This repo is in active development but I should be coming out with full a release soon.InstallInstall using pip:pip install transformer_vaeUsageYou can exececute the module to easily train it on your own data.python-mtransformer_vae\--project_name="T5-VAE"\--output_dir=poet\--do_train\--huggingface_dataset=poems\Or you can import Transformer-VAE to use as a package much like a Huggingface model.fromtransformer_vaeimportT5_VAE_Modelmodel=T5_VAE_Model.from_pretrained('t5-vae-poet')TrainingSetupWeights & Biasisfor logging, seeclient.Get a dataset to model, must be represented with text. This is what we will be interpolating over.This can be a text file with each line representing a sample.python-mtransformer_vae\--project_name="T5-VAE"\--output_dir=poet\--do_train\--train_file=poems.txt\Alternatively seperate each sample with a line containing only
seperating samples:python-mtransformer_vae\--project_name="T5-VAE"\--output_dir=poet\--do_train\--train_file=poems.txt\--multiline_samplesAlternatively provide a Huggingface dataset.python-mtransformer_vae\--project_name="T5-VAE"\--output_dir=poet\--do_train\--dataset=poems\--content_keytextExperiment with different parameters.Once finished upload to huggingface model hub.# TODOExplore the produced latent space usingColab_T5_VAE.ipynbor vising thisColab page.ContributingInstall with tests:pip install -e .[test]Possible contributions to make:Could the docs be more clear? Would it be worth having a docs site/blog?Use a Funnel transformer encoder, is it more efficient?Allow defining alternative tokens set.Store the latent codes from the previous step to use in MMD loss so smaller batch sizes are possible.Feel free to ask what would be useful!
Package: transformerx
TransformerXTransformerXis a Python library for building transformer-based models.It comes with multiple building blocks and layers you need for creating your model.JoinTensorOpscommunity on DiscordInstallationInstall the latest version ofTransformerX:Note:Currently you need to build it from the source, but, we are actively working, and it will be ready to use soon[not yet working] $ pip install transformerxFeaturesSupport CPU/GPUVectorized operationsStandard APIIf you found it helpful, please give us a:star:LicenseReleased under the Apache 2.0 licenseCopyright © 2021-2022TensorOpsDevelopersSoran Ghaderi(soran.gdr.cs@gmail.com)
follow me onTaleb Zarhesh(taleb.zarhesh@gmail.com)
follow me on
Package: transformix
pytransformixA python3 wrapper fortransformix, a command-line utility provided as part of the packageelastixCurrently runs on MacOS and Linux.InstallationStep 1Eitherpip installthis package directly from GitHub:pip install git+https://github.com/jasper-tms/pytransformix.gitorfirstgit cloneit and thenpip installit from your clone:cd ~/repos  # Or wherever on your computer you want to download this code to
git clone https://github.com/jasper-tms/pytransformix.git
cd pytransformix
pip install .Step 2Installelastixby first downloading it from thereleases page. Operating system compatibility notes:Ubuntu 20.04: Use elastix-5.0.1 or the latest version.Ubuntu 18.04: Use elastix-5.0.0Ubuntu 16.04: Use elastix-4.9.0MacOS: elastix-5.0.1 worked on Big Sur, and I haven't tested other combinations. Probably safe to download the latest version.Then extract the.zipor.tar.gzfile you downloaded and put the folder somewhere on your computer. Then add that folder'sbinsubdirectory to your shellPATHand that folder'slibsubdirectory to your shellLD_LIBRARY_PATH. For example, if you put the folder at~/software/elastix-5.0.1-linux, then add these three lines of textexport PATH=~/software/elastix-5.0.1-linux/bin:$PATH
export LD_LIBRARY_PATH=~/software/elastix-5.0.1-linux/lib${LD_LIBRARY_PATH+:$LD_LIBRARY_PATH}
export DYLD_LIBRARY_PATH=~/software/elastix-5.0.1-linux/lib${DYLD_LIBRARY_PATH+:$DYLD_LIBRARY_PATH}to your shell config file (~/.bashrcfor bash on Linux,~/.bash_profilefor bash on Mac, or~/.zshrcfor zsh on Mac). Then open up a new terminal and runelastix. If you seeUse "elastix --help" for information about elastix-usage., you're good to go. If not, feel free toopen an issueand I can try to help.
Package: transformpy
transformpyis a Python 2/3 module for doing transforms on “streams” of data.
The transforms can be applied to any python iterable object, and so can be used
for continuous real_time streams or static streams (such as from a file). It
is designed in such a manner that it uses very little memory (unless necessary
by clustering and/or aggregation routines). It was originally designed to
allow python transformations (maps and reductions) of data stored within HIVE,
using the Hadoop streaming paradigm.NOTE:TransformPy isnotguaranteed to be API stable before version 1.0;
but changes should be small if any to the current version.
Package: transform-rgbgray
Pacote: image_transform_rgbgrayPacote desenvolvido a partir de um desafio de projeto naDIO.
O pacote lê uma imagem, as transforma tingindo-as na cor especificada e salva. As imagens podem ser tingidas nas seguintes cores:Cinza;Vermelho;Azul;Amarelo;Verde.🛠 TecnologiaInstalaçãopipinstallimage_transform_rgbgrayUsofromimage_transform_rgbgray.processingimporttransformationfromimage_transform_rgbgray.utilsimportioimage=io.read_image(path_image)transformation.gray(image)transformation.red(image)transformation.blue(image)transformation.yellow(image)transformation.green(image)AuthorJorge MagnoContato:
Package: transforms3d
Code to convert between various geometric transformations.Composing rotations / zooms / shears / translations into affine matrix;Decomposing affine matrix into rotations / zooms / shears / translations;Conversions between different representations of rotations, including:3x3 Rotation matrices;Euler angles;quaternions.We have tried to document the algorithms carefully and write clear code in the
hope that this code can be a teaching reference.  We document the math behind
some of the algorithms usingsympyintransforms3d/derivations.  We would be very pleased if y’all would like to
add your own algorithms and derivations - please get a copy of the code fromhttps://github.com/matthew-brett/transforms3dand get on down,
algorithmically.  Feel free to use the github issue tracker and pull request
system to ask for advice and support.DocumentationDocumentation for latest released version athttp://matthew-brett.github.io/transforms3dCodeSeehttps://github.com/matthew-brett/transforms3dReleased under the BSD two-clause license - see the fileLICENSEin the
source distribution.Much of the code comes fromtransformations.pyby Christoph
Gohlke, also released under the BSD license.We use Github actions to test the code automatically under Pythons 3.7 through
3.10.We depend on numpy >= 1.15.  You may be able to make it work on an earlier
numpy if you really needed that.The latest released version is athttps://pypi.python.org/pypi/transforms3dSupportPlease put up issues on thetransforms3d issue tracker.
Package: transforms-fin
transforms-finOverviewA set of Transforms meant for financial analysis to be used with the datacode packageGetting StartedInstalltransforms_fin:pip install transforms_finA simple example:importtransforms_fin# transforms are automatically loaded upon importimportdatacodeasdca=dc.Variable('a','A')vc=dc.VariableCollection(a)# Portfolio transformvc.a.port()LinksSee thedocumentation here.AuthorCreated by Nick DeRobertis. MIT License.
Package: transgit
TransGitTransGit is a tool created to facilitate the export of repositories from
a GitLab instance to GitHub.transgit -u <GITLAB-URL> -t <GITLAB-TOKEN> -gu <GITHUB-USER> -p <GITHUB-PERSONAL-TOKEN>The tool clones all repositories to your machine and then performs the
export process to GitHub using the REST API.FeaturesExport GitLab repositories to personal or organization GithubExport the repositories of specific groupsArchive repositories after exportCompatibility with Git filter-repoAllows you to change the information of the commits such as, for
example, the user name, e-mail and among others.InstallIf you have Pip installed on your system, you can use it to install the
latest TransGit stable version:$ sudo pip3 install transgitOnly versions >=3.5 are supported.DisclaimerThis software must not be used on third-party servers without
permission. TransGit was created to ease the process of migrating Git
instances.
Package: trans-google-bing
translation_google_bing###free Bing and Google translation API####trans-google-bing
Version 0.0.2
A free and unlimited python API for google translate.
It's very easy to use and solve the problem that the old api which use tk value cannot be used.
This interface is for academic use only, please do not use it for commercial use.Version 0.0.2 have fixed url translate. Ps: If your get translations for different genders, it will return a list.https://support.google.com/translate/answer/9179237?p=gendered_translations&hl=zh-Hans&visit_id=637425624803913067-1347870216&rd=1####Installationpip install trans-google-bing####Basic Usage of Google Translation
#####Translatefromgoogle_bing_transimportGoogle_translatortranslator=Google_translator()text=translator.translate('你好',dest='en')print(text)->Hello####Advanced Usage
#####Translatefromgoogle_bing_transimportGoogle_translatorYou can set the service_url according to your needs. for example: service_url= "translation.google.com"it is default "translation.google.cn" if you not to set service_urltranslator=Translation(service_url="translation.google.com")default parameter : dest=auto src=autoFor src param is not to set, because API can automatically identify the src translation languagefromgoogle_bing_transimportGoogle_translatortranslator=Google_translator()text=translator.translate('你好',dest='en')print(text)->hello#####Detectfromgoogle_bing_transimportGoogle_translatordetector=Google_translator()text=detector.detect("こんにちは")print(text)->['ja','japanese']####Basic Usage of Bing Translation
#####Translatefromgoogle_bing_transimportbing_translatortranslator=bing_translator()text=translator.translator('你好',to='en')print(text)->Hello#####Detectfromgoogle_bing_transimportbing_translatordetector=bing_translator()text=detector.detect("こんにちは")print(text)->'ja'Prerequisites
Python >=3.6
requests
Package: transidate
TransidateTransidate is a commandline tool for validating transit data files such as TransXChange
NeTEx and SIRI.Transidate can validate several transit data formats out of the box.CompatibilityTransidate requires Python 3.7 or later.InstallingInstall transidate usingpipor any other PyPi package manager.pipinstalltransidateValidate an XML fileTransidate comes with a help guide to get you started. This will list all the options as
well as the transit data formats that are supported.transidate--helpTo validate a data source just specify the path to the data and the schema to validate
the data against. If the--versionis not specified the data is automatically
validated again TransXChange v2.4.transidatevalidate--versionTXC2.4linear.xmlIf transidate finds any schema violations it will print the details of the violation
such as the file it occurred in, the line number of the violation and details.Validate many files at onceYou can also use transidate to validate a archived collection of files.transidatevalidate--versionTXC2.4routes.zipThis will iterate over each XML file contained within the zip and collate all the
violations.Export violations to CSVSchema violations can be saved to a CSV file using the--csvflag.transidatevalidate--versionTXC2.4--csvroutes.zipConfigurationTransidate comes configured with several schemas out of the box. It is really
easy to add your own schema validators totransidate. The first step is to
create a configuration file e.g.touch transidate.cfg.Transidate fetches schemas from web in a zip format, to add a schema you
just need to define the name, url and root.[MYSCHEMA] # The 'version'url=http://linktoschema.url/schema.zip# where transidate can get the schemaroot=schema_root_file.xml# the root of the schemaThen you can just pass the schema configuration using--schemas.transidatevalidate--versionMYSCHEMA--schemastransidate.cfglinear.xmlYou can list all the avialble schemas list thlistcommand.transidatelist
Package: transient-display-data
transient-display-datafor Jupyter LabThis is a JupyterLab extension that allows JupyterLab to receive messages in a newtransient_display_datatypeand display them in the console window of the associted notebook.As summarizedhere,
the transient display data messages are designed to send messages that are transient
in nature and will not be displayed and saved with the notebooks. Such messages
include but not limited to status or progress information for long calculations, and
debug information. This message type is identical todisplay_datain content so you
only need to use message typetransient_display_datainstead ofdisplay_datato
mark the message as transient.This new message type is currently under review. However, even before it is officially
accepted, kernels can send messages of this type safely because all Jupyter clients
ignore messages of unknown types, and JupyterLab with this extension will be able to
display them. An an example, theSoS Kerneluses this message type to send progress information during the execution of theSoS workflows.How to installIf you are using conda version of JupyterLab, you can install this extension with commandcondainstalljupyterlab-transient-display-data-cconda-forgeOtherwise you can install thetransient-display-dataextension using commandjupyterlabextensioninstalltransient-display-dataor go to the extension manager, search fortransient-display-data, and install.How to usetransient_display_dataAfter you installed this extention, you can test it byCreate a notebook with Python 3 kernelRight click and selectNew Console for Notebookto create a console windowRight click on the console window and you selectShow Transient Message.In the Python notebook, enterkernel = get_ipython().kernel
kernel.send_response(kernel.iopub_socket,
                     'transient_display_data',
                     {
                         'data': {
                             'text/plain': 'I am transient'
                         }
                     }
                    );and a messageI am transientshould be displayed in the console window.If you are interested in tryingSoS Notebook, you can clickthis linkto start
a JupyterLab session on our live server. You can create a new notebook
with SoS kernel, open a console window, and execute for example a trivial workflow%run
[1]
[2]
[3]You can see progress messages in the console window.
Package: transientNamer
transientNamerPython API for reading and caching TNS reports.Documentation for transientNamer is hosted byRead the Docs(development versionandmaster version). The code lives ongithub. Please report any issues you findhere.FeaturesHow to cite transientNamerIf you usetransientNamerin your work, please cite using the following BibTeX entry:@software{Young_transientNamer,author={Young, David R.},doi={10.5281/zenodo.7970680},license={GPL-3.0-only},title={{transientNamer}},url={https://github.com/thespacedoctor/transientNamer}}
Package: transifex
Transifex: The Open Translation PlatformTransifex is a highly scalable localization platform with a focus on
integrating well with the existing workflow of both translators and developers.It aims in making it dead-simple for content providers to receive quality
translations from big translation communities, no matter where the project
is hosted.http://www.transifex.comGoalsProvide an interface for translators to submit translations to multiple
projects, regardless of the type of the underlying VCSReduce the overhead a project maintainer usually undertakes to administrate
accounts for translatorsHelp translators do more by eliminating the need to subscribe to each VCS and
learn its commands and tricksEncourage collaboration between developers and maintainers and thus, increase
the language coverage of the participant projectsDocumentationFor full documentation, including installation instructions, please refer tohttp://help.transifex.com/.
Package: transifex-client
Transifex Command-Line Tool⚠️ A new client, compatible with thenew API, is under development. Check out the Alpha versionhere.Getting startedWhether you have experience with the command line or not,this interactive tutorialis intended for everyone who wishes to learn how the Transifex client works. There is no need to download anything - Just click on the link provided above, and follow the instructions.For more information about TX client, please visit ourdocumentation guide here.DescriptionThe Transifex Command-line Tool enables you to manage your translations within a project without the need of an elaborate UI system.You can use the command line tool to create new resources, map locale files to translations, and synchronize your Transifex project with your local repository. Translators and localization managers can use it to handle large volumes of translation files.  The Transifex Command-line Tool can help to enable continuous integration workflows and can be run from CI servers like Jenkins and Bamboo.Click  herefor complete documentation on the Transifex Command-line Tool via our documentation site.InstallationYou can install the latest version of transifex-client runningpip install transifex-clientoreasy_install transifex-client.Build transifex-client for WindowsDownload transifex-client sources via git or github archive.git clone https://github.com/transifex/transifex-client.gitDownload and unpackhttps://github.com/transifex/transifex-client/archive/master.zipDownload and installPython.At this step choose the right version of python (2.7, 3.5, 3.6 or 3.7) and x86 or x86-64 instruction set.Make sure pip marked for installation(default for latest installers).InstallPyInstaller.Suppose that Python installed toC:\\Program Files\\Python35-32Makepython.exeaccessible via PATH environment variable or cd to directory containing python.exe.python -m pip install pyinstallerThis command will installPyInstallerpackage and its dependencies.Buildtransifex-clientdistribution.Change directory to transifex-client folder and run command:python -m PyInstaller contrib/tx.spec
# or
pyinstaller contrib/tx.spectx.exedist/tx.exewill be created as the result of build process.Getting HelpYou can always get additional help viaGitHub issuesorTransifex supportLicenseTransifex Client is primarily distributed under the terms of the GPL License (Version 2.0).SeeLICENSEfor details.
Package: transifex-python
Transifex Python ToolkitTransifex Python Toolkit is a collection of tools that allow you to easily localize your Django and Python applications using Transifex. The toolkit features fetching translations over the air (OTA) to your apps.This project adheres to the Contributor Covenantcode of conduct. To contribute to Transifex Python Toolkit, please check out thecontribution guidelines.Upgrade to v2If you are upgrading from the1.x.xversion, please read thismigration guide, as there are breaking changes in place.Quick starting guideInstall toolkit in your code$ pip install transifex-pythonAdd a provided TOKEN and SECRET in your config, connecting your application with a Transifex projectAdd internationalization commands in your code<!-- Django app template example -->{%loadtransifex%}<p>{%t"Hello!"%}</p><p>{%t"I want to be translated."%}</p># Django view samplefromtransifex.native.djangoimporttoutput={"msg1":t("Welcome aboard!"),"msg2":t("It's great to have you here!"),}returnJsonResponse(output)Push strings to your connected Transifex project./manage.py transifex pushWhen translations are added in your Transifex project are automatically made availableTo learn more about using Transifex Python toolkit check:Quickstart guide for Django web applicationQuickstart guide for Python applicationFor a general overview visitTransifex Native overviewFor some common questions & answers check ourTransifex Native communityLicenseLicensed under Apache License 2.0, seeLICENSEfile.