Saravanan666/pypi-org huggingface dataset:
Package: t5-podcast-summariser
T5 Podcast SummariserThe aim of this project is to demonstrate the capabilities of Google's T5 model that has been fine-tuned on Spotify's Podcasts Dataset for automatic podcast summarisation. Simply pass in a podcast's transcript as input to the summariser, and it would output a summary containing the gist of what the podcast is about.T5 and Spotify Podcast DatasetTo learn more about T5, readthis blog postTo learn more about the Dataset, read itspaperUsageThis package relies on theHuggingFace NLP Libraryto work so you would have to install it too:pip install transformers
pip install t5-podcast-summariserYou can now use the package as follows:from t5_podcast_summariser import Summariser
summariser =  Summariser()

transcript = """
Full Transcript of the podcast.....
"""

summary = summariser.summarise(transcript)
print(summary)Below is a sample summary generated on a podcast transcript availablehere:This week on the podcast, we talk to Ayodeji Ogunnia (@Ayodeji_Ogunnia) about his life as a bricklayer in Baltimore. We also hear from one of the most famous people in the world, and how he came to be a successful bricklayer. This is a great story for anyone who wants to learn more about what it means to be a good bricklayer. If you like what we do, please leave us a review on Apple Podcasts! Thanks for listening!Future WorkIn the next update, I will make it possible for the top-n sentences to be extracted
from the transcript before a summary is generated.
Package: t5s
title: t5s
emoji: 💯
colorFrom: yellow
colorTo: red
sdk: streamlit
app_file: app.py
pinned: falset5sT5 Summarisation Using Pytorch Lightning, DVC, DagsHub and HuggingFace SpacesHere you will find the code for the project, but also the data, models, pipelines and experiments. This means that the project is easily reproducible on any machine, but also that you can contribute data, models, and code to it.Have a great idea for how to improve the model? Want to add data and metrics to make it more explainable/fair? We'd love to get your help.InstallationTo use and run the DVC pipeline install thet5spackagepip install t5sUsageFirstly we need to clone the repo containing the code so we can do that using:t5s cloneWe would then have to create the required directories to run the pipelinet5s dirsNow to define the parameters for the run we have to run:t5s start [-h] [-d DATASET] [-s SPLIT] [-n NAME] [-mt MODEL_TYPE]
                 [-m MODEL_NAME] [-e EPOCHS] [-lr LEARNING_RATE]
                 [-b BATCH_SIZE]Then we need to pull the models from DVCt5s pullNow to run the training pipeline we can run:t5s runBefore pushing make sure that the DVC remote is setup correctly:dvc remote modify origin url https://dagshub.com/{user_name}/summarization.dvc
dvc remote modify origin --local auth basic
dvc remote modify origin --local user {user_name}
dvc remote modify origin --local password {your_token}Finally to push the model to DVCt5s pushTo push this model to HuggingFace Hub for inference you can run:t5s uploadNext if we would like to test the model and visualise the results we can run:t5s visualizeAnd this would create a streamlit app for testing
Package: t61codec
For information about the codec seehttps://en.wikipedia.org/wiki/ITU_T.61InstallationInstallation follows the standard Python procedure:pip install t61codecThe package uses Semantic Versioning 2.0 (https://semver.org/spec/v2.0.0.html).UsageThe codec can be registered into Python’s codec registry. A helper method has
been provided:import t61codec
t61codec.register()Please see the notes oncodecs.register!After registering, the codec is available as either't61'or't.61':>>> b'Hello T.61: \xe0'.decode('t.61')
'Hello T.61: Ω'
>>> 'Hello T.61: Ω'.encode('t.61')
b'Hello T.61: \xe0'
Package: t64
t64This Python module enables read access to C64s tape container files (.t64).It provides methods to list and extract the files stored within a container, only PRG files are currently supported.ExamplesClasses reside in thet64module, the whole module may be imported or just those definitions referenced by the user.Displaying the contents of a containerTo perform a directory listfromt64importT64ImageReaderwithT64ImageReader('example.t64')ast:forlineint.directory():print(line)TODOdetailed documentationbetter docstringsmore examples
Package: t77-date
A set of functions related with datesInstallationInstall viapippip install t77_datedatetime modulestart_of_dayReturn a new datetime with values that represent a start of a day.Example>>> dt = datetime(2016, 7, 2, 21, 49, 12)
>>> sod = start_of_day(dt)
>>> print(sod)
2016-07-02 00:00:00end_of_dayReturn a new datetime with values that represent a end of a day.Example>>> dt = datetime(2016, 7, 2, 21, 49, 12)
>>> eod = end_of_day(now)
>>> print(eod)
2016-07-02 23:59:59.999999start_of_monthReturn a new datetime with values that represent a start of a month.Example>>> dt = datetime(2016, 7, 2, 21, 49, 12)
>>> som = start_of_month(dt)
>>> print(som)
2016-07-01 00:00:00end_of_monthReturn a new datetime with values that represent a end of a month.Example>>> dt = datetime(2016, 7, 2, 21, 49, 12)
>>> eom = end_of_day(now)
>>> print(eom)
2016-07-31 23:59:59.999999set_next_week_daySet week day.
New date will be greater or equal than input date.Example>>> saturday = datetime(2016, 7, 2, 21, 49, 12)
>>> next_friday = set_next_week_day(saturday, ISO_FRIDAY, iso=True)
>>> print(next_friday)
2016-07-08 21:49:12
>>> next_friday = set_next_week_day(saturday, FRIDAY, iso=False)
>>> print(next_friday)
2016-07-08 21:49:12set_prev_week_daySet week day.
New date will be less or equal than input date.Example>>> saturday = datetime(2016, 7, 2, 12)
>>> prev_friday = set_prev_week_day(saturday, ISO_FRIDAY, iso=True)
>>> print(prev_friday)
2016-07-01 21:49:12
>>> prev_friday = set_prev_week_day(saturday, FRIDAY, iso=False)
>>> print(prev_friday)
2016-07-01 21:49:12
Package: t8s
t8sTable of ContentsInstallationTestingPublishingLicenseInstallationpipinstallt8s# Edit your code using t8s.ts.TimeSerie and others related classesCheck Linter rules usingPyRight(https://microsoft.github.io/pyright)pyright--levelwarning.Testing# Para configurar o ambiente de testes, define a variável de ambiente# T8S_WORKSPACE_DIR, por exemplo:exportT8S_WORKSPACE_DIR=/Volumes/dev/t8sVeja tambémBDD# Para inspecionar a configuração do ambiente de testes:
hatch config show
hatch clean
hatch build
# Edit your main.py code
hatch run python3 main.py
./test-all.sh
# Usando BDD com behave (https://behave.readthedocs.io/en/latest/)
rm logs/timeseries.log
hatch run python -m behave --logging-level INFO --no-capture --no-capture-stderr --no-skipped features
cat logs/timeseries.logPublishinghatchpublishLicenset8sis distributed under the terms of theMITlicense.
Package: t9ek80
EK80 SN90Simrad EK15/60/80 and SN90 echosounder python interface module. The t9ek80.py should normally not be modified,
it contains the generic interface. The biomass and singletargetchirp are example user files. The xml files are
configuration files. Usage:
pip install --index-urlhttps://test.pypi.org/simple/--no-deps t9ek80orsimply copy the t9ek80.py to the same folder as the code you run... Thats all.git clonehttps://github.com/The1only/ek80.gitcd ./ek80/examples
python3 biomass.py biomass.xmlPS: All testing has been done using python3The Simrad EK80 user manual can be found at:https://www.simrad.online/ek80/ref_en/default.htm
Package: t9keyboard
T9 Keyboard controled by Numpad keyboard buttons.Project of T9 keyboard which is controlled by numpad keyboard buttons.
Application takes sequence of digits, convert it to corresponding letters combos and use Trie tree search to match sequence with potent words.
When user choose a phrase from available predicted words search results, it will be written to the screen.PreviewUsing application to type in notepad:Instalation guide and system requirementsTo run project this code, you need Python 3.10 + (usage of match/case feature).Program runs only onwindowsplatform (due to problems on Linux keys signal suppression)cd<mainprojectdirectory>pipinstall-rrequirements.txtpythonrun.pyKey bindingsDefault key actions for T9 mode.
    """
    +-------+-------+-------++-------+
    |   7   |   8   |   9   ||   +   |
    |  .?!  |  ABC  |  DEF  ||       |
    +-------+-------+-------+|       |
    |   4   |   5   |   6   || BACK  |
    |  GHI  |  JKL  |  MNO  || SPACE |
    +-------+-------+-------++-------+
    |   1   |   2   |   3   |
    | PQRS  |  TUV  |  WXYZ |
    +-------+-------+-------+
    |       0       |   .   |
    |     SPACE     |SWITCH |
    +-------+-------+-------+
    """
    Digit key 7 :
        - type Dot sign
    Digit keys 8, 9, 4, 6, 7, 1, 2, 3 :
        - Add digit to queue.
    Dot key (Del):
        - switch word hint (phrase)
    Digit key 0 :
        - Accept current hint phrase. Then write phrase as keyboard output
    Plus key + :
        - BackspaceRunning TestsPytest is configured to be runned from main project directory.
To run tests, use the following command.cd<mainprojectdirectory>pytest
Package: t9text
t9textA Python package for working with T9 text messaging.Descriptiont9textis meant for two main purposes:Given a string of alphabetic text (plus-,*,#),t9textcan tell a user which numeric touchtone phone buttons correspond to it, e.g.:fromt9textimportt9daily_show='1-210-WH-CRIME'# The Daily Show's phone number for Trump to confess his crimes to :-)buttons=t9.get_touchtone(daily_show)print(buttons)# prints "1-210-94-27463"Given a string of 10 or fewer digits, display all possible T9 "words", e.g.:fromt9textimportt9digits="2332"#can also be `int` such as 2332words=t9.get_words(digits)forwordinwords:print(word)# ["adda", addb", "addc", "adea" ...]
Package: taal
TaalTaal is a framework for translating your data. It plugs in to e.g.SQLAlchemyorKaiso, providing aTranslatableStringfield type and a mechanism for storing and retrieving content in multiple
languages.For use-cases where the most common interaction with the translated data is for
reading, an application can be set up so that language context and translations
are handled centrally, after which business logic can be written almost as it
would for a single-language app.PhilosophyTaal uses a two-phase process for managing translatable data. Upon retrieval,
data is marked up as “requires translation”. Subsequently (typically higher up
in the stack, e.g. in some middleware), information about which particular
language we are interested in may be supplied to find the actual translation
string.Example useclass MyModel(Base):
    __tablename__ = "my_model"

    id = Column(Integer, primary_key=True)
    name = Column(TranslatableString())>>> instance = session.query(MyModel).first()
>>> instance.name
<TranslatableString: (...)>

>>> translator = get_translator('en')
>>> translator.translate(instance.name)
"Spam"DevelopmentTo make your life easier, create asetup.cfgfile with a[pytest]section to define your database and neo4j connection strings:$ cat setup.cfg
[pytest]
addopts= --neo4j_uri=http://... --db_uri=mysql://...(Note that pytest gets upset if you indent theaddoptsline)
Package: taatik
taatik - translitertion of filenames from Herbrew letters to Latin letters.===========================================================================Latinize Hebrew filenames, for use in unicode-incompatible devicessuch as old music players or car cd players. Supports two transliterationstables:*   EKTB as suggested by [Amnon Katz in 1987][AK].Characters are identified by the early Hebrew letters from which they developed.converts   אבגדהוזחטיכלמנסעפצקרשתךםןףץto         ABCDEFGHVIKLMNXOPZQRSTKMNPZfor example : הרחפת שלי מלאה בצלופחים ->  ERHPT SLI MLAE BZLFPHIM*   PHONETICbased on similar sounding letters, except ט->U ש->W which are based on figure.converts   אבגדהוזחטיכלמנסעפצקרשתךםןףץto         ABGDHVZXUYKLMNSOPCQRWTKMNPCfor example : הרחפת שלי מלאה בצלופחים ->  HRXPT WLY MLAH BCLVPXYMNote that both schemes do not treat the five final letters differently.[AK]:  dx.doi.org/10.1093/applin/9.3.306  "Article by Amnon Katz"Usage--------usage: taatik [-h] [-r] [-q] [-t {EKTB,PHONETIC}] FILENAME [FILENAME ...]positional arguments:FILENAME              the filenames to renameoptional arguments:-h, --help            show this help message and exit-r, --really-rename   really rename the files. if not specified, the programwill only show what would have happend-q, --quiet           do not print anything-t {EKTB,PHONETIC}, --table {EKTB,PHONETIC}use the specified translation table (default: EKTB)
Package: tab2img
Tab2Img: from tabular data to imagesA tool to convert tabular data into images for CNN. Inspired by theDeepInsightpaper.Installationpip install tab2imgBackgroundIn thepaper"DeepInsight: A methodology to transform a non-image data to an image for convolution neural network architecture" the autors propose  a method to convert tabular data into images, in order to utilize the power of convolutional neural network (CNN) for non-image structured data.The Figure illustrates the main idea: given a training datasetwithsamples andfeatures, we are required to find a function, where.There are numerous ways to choose. In this implementation, the features are organized with respect to the correlation vector, whereis the target vector.
Givenandasthe vectorexpress the Pearson correlation coefficient*whereAt this pointis sorted from the greatest to the smallest, generating the vector of indicesEventually, the final tensorisThe function that mapsto the right row and columnofisIn this case, beinga sample, the coefficient is implemented asExamplefromsklearn.datasetsimportfetch_covtypefromtab2img.converterimportTab2Imgdataset=fetch_covtype()train=dataset.datatarget=dataset.targetmodel=Tab2Img()images=model.fit_transform(train,target)
Package: tab2latex
If you got a problem or suggestions to improve the scripts, please create anissue in git.Usingtextable:textable.latex_tab(data=[arr1,arr2],names=[r"title column 1",r"title column 2"], filename=r"example.tex",caption=r"Beautiful caption",label=r"important_label",dec_points=[2,0])Where data_array must be an array of arrays; names_array must be an array of strings, containing the column-names, filename has to be a string,too.dec_points sets precision for each column. dec_points is ignored, if uncertainty-arrays are used as input. Instead, the rounding rules by Particle Data Group are used (as in uncertainties-package implemented)For an example, have a look at example.pylabel must be a string.To write long tables, usetextable.long_tab(data=[arr1,arr2],names=[r"title column 1",r"title column 2"], filename=r"example.tex",caption=r"Beautiful caption",label=r"important_label",dec_points=[2,0])Btw: You do not need to add prefix "tab:".If you want to see the results, compile the .tex-file.
To do so, easiest way is to use the makefile.
That will compile main.tex where the generated example.tex is included.Version:
0.0.5: Fixed errors with uncertainties
Package: tabarnak-desjare
tabarnak - transcoderFFmpegbased utility toolDescriptionIn the Province of Québec tabarnak is a swear derived from the tabernacle but it is an acronym for:TranscoderAllBasicallyAccessibleResolutelyNotAllKlingonLe tact dans l'audace, c'est de savoir jusqu'où on peut aller trop loin.Jean CocteauThis script is used to convert video or audio media files to modern codecs such as HEVC, VP9, AV1 (experimental) or Opus. It walks into a directory and probes media files for video or audio that are not encoded with specified codec (default HEVC). Then it transcodes them. The idea is to use a more modern codec that has a better quality/bitrate ratio. Resulting files are significantly smaller than original h264, mpeg2, mpeg4 or likely any other formats that they are currently encoded to.tabarnak usescrfencoding. All channels are copied. The audio is converted by ffmpeg to ogg vorbis by default.tabarnak won’t delete or clean up your files. It will skip files that are already done. Basic sanity check is performed after encoding and it will print a warning if the input and output size do not match.If you plan to use your media files with a chrome cast, be sure the buychromecast ultrasince the 3rd generation chromecast does not support HEVC.FeaturesEasy to use out of the boxinput & output directory supportkeep relative path option for transcoding entire hierarchy of media filescopy your metadata (default) or notBasic default configuration for basic codecs such as h264, hevc, vp9 & opusCustom configuration workflow using either json or yamlConfigurable logging including basicprometheussupportTranscoder stats output (yaml)Automatic testsUsageInstallpip3 install tabarnak-desjareGetting helpFor help:
tabarnak -hExample usagetranscode all media files recursively using h264 codec and output them to the output directorytabarnak --input-dir . --output-dir output --h264transcode your entire music library recursively using opus codec in ogg container while keeping your directory structure and copy non media filestabarnak --copy --opus --keep-relative-path --map-args "-map 0:0 " --output-dir your_output_dirNote that the mapping argument is necessary since iTunes files may contain unwanted tracks that prevent files to be recognised by music players.Requirementspython3(tested with python 3.7 and 3.8)ffmpegin your pathI prepared a tutorial video to learn how to do that on Windows 10:https://gist.github.com/desjare/adc1514d46bcb38414a5e2a602f6d12d
Package: tabassist
TABASSISTWhat's is aboutThis package has aim to assist you while you work with Tableau and takes some routine from your shoulders.INSTALATIONpip install tabassistUSAGE// Check Tableau workbook for errors$tabassistcheck--file"/Users/user_name/Tableau_workbook.twbx"T100: 'check_NOT_in_lowercase' field not in lowercaseT101: ' check_trailing_whitespace' field has trailing whitespaceT101: 'check_trailing_whitespace ' field has trailing whitespaceT102: 'check_non_unique_name (copy)' field has non allowed symbol(s)T102: 'check_non_allowed_символы' field has non allowed symbol(s)T103: 'check_non_unique_name (copy)' field has '(copy)' as a part of nameT104: 'check_comment_doesnt_exist' field doesn’t have commentT105: 'check_comment_position' field has inline comment which doesn’t start from first lineT106: 'id' field doesn’t used by any worksheetT106: 'check_unused_by_worksheet' field doesn’t used by any worksheetT106: 'city' field doesn’t used by any worksheet// Export documentation of Tableau workbook$tabassistdocexport--file"/Users/user_name/Tableau_workbook.twbx"--namedocumentaion.mdDocument exported to '/Users/user_name/'.documentaion.mdОписание дашбордаto be updated...Кто пользуется дашбордомto be updated...Частота обновленияto be updated...MOCK_DATAМестонахождениеСервер -Тип БД - textscanБаза данных - NoneПорт - NoneОписание полейНаименованиеОписаниеФормулаidNonefirst_nameNonelast_nameNoneemailNonegenderNoneip_addressNonebirth_dateNoneorder_dateNonecityNonecityNoneidNoneages// age of fake clientDATEDIFF('year', [birth_date], >TODAY())check_NOT_in_lowercase// check field name in lowercase'fake'check_non_unique_name (copy)// check stop words'fake'check_non_allowed_символы// check non allowed symbols'fake'check_comment_doesnt_exist'fake'check_unused_by_worksheet// check field is unused'fake'check_trailing_whitespace// check field name for containing >whitespace after'fake'check_comment_exist// check commect exist'fake'check_comment_position'fake'// check comment starts from first&>ensp;linecheck_trailing_whitespace// check field name for containing >whitespace before'fake'first_nameNonelast_nameNoneemailNonegenderNoneip_addressNonebirth_dateNoneorder_dateNoneKnown issues and workaroundnot found yet.DEVELOPMENTvirtualenv envsource ./env/bin/activatepip install -r requarements.txt(OPTIONAL)pip install pycodestylepip install pydocstylepip install pytest
Package: tabatu
tabatuDefinição de tábuas atuariais em python com códigos em C++ para melhor performance.InstalaçãoO pacote pode ser instalado diretamente do PyPI:pip install tabatuUsoO pacote fornece 3 classes para lidar com tábuas atuariais:Tabua,TabuaMDTeTabuaMultiplasVidas.Uma tábua pode ser criada diretamente a partir de um array de taxas.>>> from tabatu import Tabua
>>> qx = [0.1, 0.2, 0.3, 0.4, 0.5, 1.0]
>>> tabua = Tabua(qx)
>>> tabua.tpx([2], [1,2,3])
array([0.7 , 0.42, 0.21])
tabua.t_qx([2], [1,2,3])
array([0.28, 0.21, 0.21])A tábua fornece métodos para cálculo de probabilidades de sobrevivência e morte, além de fornecer algumas utilidades
como tempo de vida futuro máximo e um indicador a respeito do fechamento da tábua ser platô ou não.DocumentaçãoA documentação está disponívelaqui.
Package: tabben
A Python package for the Open Benchmark for Tabular DataThis is a Python package for working with the tabben benchmark for tabular data (for machine learning tasks):loading, processing, or inspecting datasets,evaluating models on test sets using consistent evaluation metrics, andexamining collections of datasets from the benchmark.See thetabben websitefor more info about the project.Set UpFor the most recentstablerelease, you can install thetabbenpackage from PyPI:pip3installtabben--upgradeIf testing/using locally from source, you can install thetabbenpackage locally; first clone this repository locally, and then install from the python subdirectory:pip3install-e.DocumentationSee thepackage docsfor tutorials, API references, and details about each of the datasets included in the benchmark.TestingAfter installingpytestand the required test dependencies, all the tests can be run by just runningpytestfrom the package directory.There is also a "large" pytest mark on tests that test with large datasets that can be used to exclude running on those datasets (as those can take a while to run, upwards of a minute depending on the network and computer).pytest-m"not large"
Package: tabbyj
tabbyjmeowj & catj, but in Python.InstallationVia Pip (Recommended)python-mpipinstalltabbyjFrom source (For development)InstallPoetrygit clone https://github.com/nint8835/tabbyj.gitcd tabbyjpoetry installPlease note that this will create a virtualenv and install the package into it. If you wish to use the package globally, please install it via Pip.UsagePipingTabbyj allows the user to simply pipe data into it in order to process it. For example:curlhttps://api.github.com/gists|tabbyjReading from a fileTabbyj allows the user to easily read the data to process from a file via a command line argument. For example:tabbyj--fileexample.json
Package: tabcell-score
TABLE CELL SCOREDescriptionThis package give a metric to evaluate table cell detection based on table cell bounding boxes.InstallationYou can install the package using pip:pipinstalltabcell_scoreUsageInput format:We follow the format of the json label file fromlabelme.{"shapes":["label":"cell","points":[[0.,0.,],[13.,13.]]]}In code use:fromtabcell_scoreimportTabCellScore# Specify the label and image pathgt_path=r"sample\gt.json"pred_path=r"sample\pred.json"img_path=r"sample\image.jpg"scorer=TabCellScore(iou_threshold=0.95)# Output score onlyscore=scorer(gt_path,pred_path)# Output score and visualize on image and save to "result.jpg"score=scorer(gt_path,pred_path,img_path,"result.jpg")# Output score and visualize on empty image and save to "result.jpg"score=scorer(gt_path,pred_path,None,"result.jpg")print(score)LicenseThis project is licensed under the Apache License 2.0 - see the LICENSE file for details."Hope this package could help you in table cell evaluation." Phạm Phú Ngọc Trai (fromakaOCR).