Saravanan666/pypi-org huggingface dataset:
Package: textatistic
Textatistic is a Python package to calculate theFlesch Reading Ease,Flesch-Kincaid,Gunning Fog,Simple Measure of Gobbledygook(SMOG) andDale-Challreadability indices. Textatistic also contains functions to count the number of sentences, characters, syllables, words, words with three or more syllables and words on an expanded Dale-Chall list of easy words.Installation$pipinstalltextatisticDocumentationDetailed documentation available aterinhengel.com.Quickstart>>>fromtextatisticimportTextatistic# Generate object of readability statistics.>>>text='There was a king with a large jaw. There was a queen with a plain face.'>>>s=Textatistic(text)# Return sentence count.>>>s.sent_count2# Return Flesch Reading Ease score.>>>s.flesch_score114.11500000000001# Return dictionary of character/word/syllable counts.>>>s.counts...# Return dictionary of readability scores.>>>s.scores...# Return dictionary of all attribute values.>>>s.dict()...Table of attributeschar_countnumber of non-space characters.notdalechall_countnumber of words not on Dale-Chall list of words understood by 80% of 4th graders.polysyblword_countnumber of words with three or more syllables.sent_countnumber of sentences.sybl_countnumber of syllables.word_countnumber of words.dalechall_scoreDale-Chall score.flesch_scoreFlesch Reading Ease score.fleschkincaid_scoreFlesch-Kincaid score.gunningfog_scoreGunning Fog score.smog_scoreSMOG score.
Package: texta-torch-tagger
TEXTA Torch Tagger Python PackageInstallationFrom Gitpip install git+https://git.texta.ee/texta/texta-torch-tagger-python.gitTestingpython -m pytest -v testsUsage>>>  from texta_tools.embedding import W2VEmbedding
>>>  from texta_torch_tagger.tagger import TorchTagger
>>>  
>>>  data = {"good": ["Täna on ilus ilm.", "Kuidas käsi käib?"], "bad": ["Arno on loll.", "ei"]}
>>>  
>>>  embedding = W2VEmbedding()
>>>  embedding.train([y for x in data.values() for y in x])
>>>  
>>>  tagger = TorchTagger(embedding, model_arch="TextRNN")
>>>  
>>>  report = t.train(data, num_epochs=3)
>>>  
>>>  tagger.tag_text("Ei hooli.")
"bad"
Package: textaudio
# text to audio file converter by python
I make a Python based text to audio file converter project. this project  is a open source project. You can download  this project.To run this project , first run :-pip install textaudioNow, for use this package, you need some packages.to use thease package, install this packages first :-pip install gttspip install playsoundpip install pyautoguiafter run those commands, run command :-make a file in anyware in desktop. ex; ‘xyz.py’.after making thease file, insert this line to the file :-from textaudio import mainthen, call the main function via :-main()then a GUI has open.now follow the GUI instructions and convert a text massage into a audio file & save in your desktop.
Package: textaug
Text-Image-Augmentor한글 이미지 인식 데이터셋을 위한 데이터 증강 라이브러리목적OCR 텍스트 인식 모델을 개발할 때, 필요한 학습 데이터셋 수를 늘리기 위한 목적으로 개발개요증강 데이터는 크게 두 가지 왜곡 방식으로 이루어져 있습니다. 하나는 글자의 형태에 변형을 가하는 형상 왜곡 방식이고, 다른 하나는 글자와 배경의 색에 변형을 가하는 색상 왜곡 방식입니다. 글씨 이미지는 색상보다, 형상에 의해 더 많은 영향을 받습니다. 이에 따라, 해당 라이브러리는 글자의 형태 변환을 위주로 이미지에 적용하도록 구성하였습니다.**CLI 환경(command line interface)**으로 구성하여 연구자들과 개발자들이 쉽게 쓸 수 있도록 구성하였고, 라이브러리는 데이미지 증강 라이브러리 중 하나인imgaug를 기반으로 작성하였습니다.설치pypi에서 패키지 설치하기pipinstalltextaugcaution : 아직 pypi에 배포하지 않은 상태입니다. Test code 작성 완료 후, 배포하도록 하겠습니다.Repository로부터 설치하기# 해당 디렉토리로부터 다운받은 후, 아래를 실행시키면 됩니다.python3setup.pyinstall사용방법해당 프로그램은 Command Line Interface(Terminal, CMD, shell) 등 환경에서 실행시킬 수 있습니다.exampletextaug<input_dir><output_dir>--multiples=3위 코드를 실행시키면,input_dir내 이미지 파일들을 읽어들여,output_dir에 증강 이미지들이 저장됩니다.input_dir내 이미지 파일 (ex 안녕.png) 이 존재한다면,output_dir에는 무작위로 증강된 3개의 이미지 (ex 0_안녕.png, 1_안녕.png, 2_안녕.png)가 저장됩니다.Option아래의 요소들은 증강 이미지의 노이즈 수준을 결정짓는 데 이용됩니다. 필요한 수준의 노이즈를 아래를 통해 정하시길 바랍니다.--multiples: 하나의 이미지 당 몇 개의 증강 이미지를 생성할 것인지 결정, 정수형으로 입력되어야 합니다.--blur: 이미지의 흐릿한 수준을 결정. 각 값에 대한 흐릿한 정도는 아래와 같습니다.blur=0blur=2blur=4blur = 6blur=8--noise: 이미지 배경에 노이즈 수준을 결정. 각 값에 대한 노이즈 정도는 아래와 같습니다.noise=0noise=1noise=20noise=30noise=40--rotate: 이미지의 회전 변환 수준을 결정. 각 값에 대한 변환 정도는 아래와 같습니다.rotate=0rotate=2rotate=4rotate=6rotate=8--shear: 이미지의 전단 변환 수준을 결정. 각 값에 대한 변환 정도는 아래와 같습니다.shear=0shear=2shear=4shear=6shear=8--elastic: 이미지의 Elastic 변환 수준을 결정. 각 값에 대한 변환 정도는 아래와 같습니다.elastic=0elastic=1elastic=2elastic=3elastic=4Copyright 2019, NIA(한국정보화진흥원), All rights reserved.
Package: text-autoencoder
# text-autoencoder[![travis][travis-image]][travis-url][![pypi][pypi-image]][pypi-url][travis-image]: https://img.shields.io/travis/Yoctol/text-autoencoder.svg?style=flat[travis-url]: https://travis-ci.org/Yoctol/text-autoencoder[pypi-image]: https://img.shields.io/pypi/v/text-autoencoder.svg?style=flat[pypi-url]: https://pypi.python.org/pypi/text-autoencoderVarious autoencoder for text data.## Usage### Grab one autoencoder first```pythonfrom text_autoencoder.variational_autoencoders import VAEXXXmodel = VAEXXX(n_steps=..., latent_size=..., state_size=..., ...)```### How to train- Warning: please preprocess your data to be a numpy array with shape (data_size, maxlen, embedding_size)```pythonmodel.fit(x=..., mask=..., epochs=10)```### How to save model```pythonmodel.save(output_path)```### How to get latent vector `z````pythonmodel.get_latent_vector(x=..., mask=..., batch_size=1)```### How to get output of encoder```pythonmodel.encode(x=..., mask=..., batch_size=1)```### How to load a trained model```pythonmodel.load(path)```### How to monitor the training process- get the output_dir you input when calling `model.fit`- monitor training loss```shell> tensorboard --logdir="<output_dir>/summary/subtrain/"```- monitor validation loss```shell> tensorboard --logdir="<output_dir>/summary/valid/"```
Package: textbase-client
✨ Textbase is a framework for building chatbots using NLP and ML. ✨Just implement theon_messagefunction inmain.pyand Textbase will take care of the rest :)Since it is just Python you can use whatever models, libraries, vector databases and APIs you want.Coming soon:PyPI packageEasy web deployment viatextbase-client deploySMS integrationNative integration of other models (Claude, Llama, ...)InstallationMake sure you havepython version >=3.9.0, it's always good to follow thedocs👈🏻1. Through pippipinstalltextbase-client2. Local installationClone the repository and install the dependencies usingPoetry(you might have toinstall Poetryfirst).For proper details seeheregitclonehttps://github.com/cofactoryai/textbasecdtextbase
poetryshell
poetryinstallStart development serverIf you're using the default template,remember to set the OpenAI API keyinmain.py.Run the following command:if installed locallypoetryrunpythontextbase/textbase_cli.pytestif installed through piptextbase-clienttestResponse:Pathtothemain.pyfile:examples/openai-bot/main.py# You can create a main.py by yourself and add that path here. NOTE: The path should not be in quotesNow go to the link in blue color which is shown on the CLI and you will be able to chat with your bot!Other commands have been mentioned in the documentation website.Have a look😃!ContributionsContributions are welcome! Please open an issue or create a pull request.
Follow ourcontributions guidefor more details!
Package: text-based-clusterer
Text Based ClustererAllows you to cluster python objects based on chosen string attributes.InstallationTo install, usepip install text-based-clusterer.UsageInstantiationYou can import the Clusterer class with the line below.fromtext_based_clustererimportClustererTo use the clusterer, first create a python class with a string attribute or use an existing python class with a string attribute.classTestClass:__init__(self,string1:str,string2:str):self.string1=string1self.string2=string2Then instantiate the Cluster object. There is an optional parametercluster_thresholdthat defines how textually similar objects should be to be clustered together.cluster thresholdranges from 0 (completely different) to 1 (exactly the same) and is set to a default of 0.3.clusterer=Clusterer(cluster_threshold=0.3)Basic ClusteringUse theadd_objectsmethod to cluster objects. You will need to pass in a list of python objects as well as a list of strings representing the attributes used for clustering. You can retrieve a list of clusters usingget_objects.test_class_1=TestClass('dog','cat')test_class_2=TestClass('cat','dog')test_classes=[test_class_1,test_class_2]single_string_clusterer=Clusterer()single_string_clusterer.add_objects(test_classes,['string_1'])single_string_clusterer.get_objects()# returns [[test_class_1], [test_class_2]]multi_string_clusterer=Clusterer()clusterer.add_objects(test_classes,['string_1','string_2'])multi_string_clusterer.get_objects()# returns [[test_class_1, test_class_2]]Adding to Existing ClustersUse theadd_clustersmethod to add an initial set of clusters in the form of nested lists. Note that this method should only be used when there are no existing clusters within theClusterobject because callingadd_clustersdoes not perform clustering.test_class_1=TestClass('dog','cat')test_class_2=TestClass('cat','dog')initial_cluster=[[test_class_1],[test_class_2]]clusterer=Clusterer()clusterer.add_clusters(initial_cluster,['string_1'])clusterer.add_objects([test_class_1],['string_1'])clusterer.get_objects()# returns [[test_class_1, test_class_1], [test_class_2]]Sources UsedThis package implements a text based incremental clustering algorithm from the research paperIncremental Clustering of News Reports.
Package: textbasedlib
Text-Based LibTBLIB is a python library made for creating text-based games in the terminalUsageTo use TBLIB, you'll need to download it from pip:pip install textbasedlibThen, you can move to your coding directory and initalize and empty TBLIB project:python -m textbasedlib new_project "Project Name"And there you go!Now you can go and explore the documentation :https://tblib-docs.devplodocus.repl.coAuthorsTBLIB was created by gugu256 (www.gugu256.github.io) and canard (www.canard101.github.io).It  uses the Pixel-Console-Image system by El1teWatermelon (www.github.com/El1teWatermelonGames) and the typewriting effect by NaolNinja (www.replit.com/@NaolNinja).
Package: text-based-switcher
===================Text-based-switcher===================Text-based window switcher for UnityThe visual switcher is very poor at providing context. E.g. the browser windowthumbnails are too small to tell apart.This switcher shows a list of window titles and allows you to choose oneby typing first character(s) of the sought window. Press return to bring thewindow to the front.All credit goes to the original author, Jacob Vlijm. Thanks!See http://askubuntu.com/a/648800/20835Usage=====The script needs ``wmctrl`` and ``xprop``. On Ubuntu:.. code:: console$ sudo apt-get install wmctrl x11-utilsInstall from PyPI:.. code:: console$ sudo pip install text_based_switcherTest-run it:.. code:: console$ list_windows --workspace$ list_windows --application$ list_windows --windowIf it works, add one or more of the preferred commands to one or more shortcutkeys: choose:_System Settings_ > _Keyboard_ > _Shortcuts_ > _Custom Shortcuts_.Click the "+" and add the command.If you do a local install (``--user`` option), Compiz won't get the rightPython path.You can fix this by hacking ``sys.path`` in the installed script.TODO====- Add wmctrl or ruamel.emwh as dependency- Add xprop as dependency- Don't call commands, use Python libraries- Write some testsDeveloper notes===============Please use a virtualenv to maintain this package, but I should not need to say that.Grab the source from the SCM repository:.. code:: console$ python setup.py develop$ pip install text-based-switcher[dev]If you installRun the tests (there are no tests yet!):.. code:: console$ python setup.py test$ python run_tests.pyLinks=====Project home pagehttps://github.com/jean/text-based-switcherSource codehttps://github.com/jean/text-based-switcherIssue trackerhttps://github.com/jean/text-based-switcher/issuesChanges log===========0.0.1------ First version by Jacob Vlijm as an answer on AskUbuntu:http://askubuntu.com/a/648800/208350.0.2------ Package creation with ``bobtemplates.gillux``[jean]
Package: textbisect
DescriptionThis Python 3 module provides functionality to search inside sorted text
files.  The lines of the files need not be all of the same length. The
module contains the following functions:text_bisect_left(a, x, lo=0, hi=None, key=lambda x: x)locates the
insertion point for linexin seekable filelike objectaconsisting of a number of lines;xmust be specified without a
trailing newline.amust use\nas the newline character and
must not perform any line endings translation (useopen(...,newline='\n')).  The parametersloandhi, if specified, must be
absolute positions within objecta, and specify which part ofato
search; the default is to search the entirea. The character pointed
to byhi(or the last character of the object, ifhiis
unspecified) must be a newline.keyis a function that is used to
compare each line ofawithx; line endings are removed from the
lines ofabefore comparison.amust be sorted or the result will
be undefined. Ifxcompares equal to a line ina, the returned
insertion point is the beginning of that line. The initial position ofais discarded. The function returns the insertion point, which is
an integer betweenloandhi+1, pointing to the beginning of a
line; when it exits,ais positioned there.text_bisect_right()is the same astext_bisect_left(), except
that ifxcompares equal to a line ina, the returned insertion
point is the beginning of the next line.text_bisect()is the same astext_bisect_right().LicenseCopyright (C) 2016 Antonis ChristofidesThis program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.You should have received a copy of the GNU General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.
Package: textbite
semANT-TextBiteTool for extracting logical chunks from complex document pages.Install from PyPi:pip install textbiteRun by simply providing a folder of images alongside a folder of corresponding PAGE XMLs (such as obtained from pero-ocr):textbite --xml-input page_xmls/ --images jpegs/ --xml-output textbite-out/ [--model best-weights.pt]By default, TextBite downloads a detection model from the internet. In case you are working in an offline environment, you candownload ityourself and provide path as an argument.
Package: text-blind-watermark
text_blind_watermarkPut message(blind watermark) into a text. so that the message is invisible, and the changes of the text are not perceptible.Video demo：https://www.bilibili.com/video/BV1m3411s7kTOnline demo(from old version, for demo only):https://www.guofei.site/pictures_for_blog/app/text_watermark/v1.html中文 readmeREADME_cn.mdSource code:https://github.com/guofei9987/text_blind_watermarkRust Version:https://github.com/guofei9987/text-blind-watermarkCan be used inWechatdingdingzhihu.com...How to Useinstall>pipinstalltext_blind_watermarkembed message into text:fromtext_blind_watermarkimportTextBlindWatermark2password='20190808'text='这句话中有盲水印，你能提取出来吗？'watermark='github.com/guofei9987'text_blind_wm=TextBlindWatermark2(password=password)text_with_wm=text_blind_wm.embed(text=text,watermark=watermark)print(text_with_wm)extract message from texttext_blind_wm2=TextBlindWatermark2(password=password)wm_extract=text_blind_wm2.extract(text_with_wm)print('提取内容：',wm_extract)github.com/guofei9987chr_typeYou can choose different type to get better performance.fromtext_blind_watermarkimportTextBlindWatermark2password='20190808'text='这句话中有盲水印，你能提取出来吗？'watermark='github.com/guofei9987'text_blind_wm=TextBlindWatermark2(password=password,chr_type=(3,4))text_with_wm=text_blind_wm.embed(text=text,watermark=watermark)print(text_with_wm)text_blind_wm2=TextBlindWatermark2(password=password,chr_type=(3,4))wm_extract=text_blind_wm2.extract(text_with_wm)print('提取内容：',wm_extract)assertwatermark==wm_extractAnother algorithm is more robustAlice Put her text watermark into a text:fromtext_blind_watermarkimportTextBlindWatermarkwatermark="绝密：两点老地方见！"text="这句话中有盲水印，你能提取出来吗？"*16password="20190808"twm=TextBlindWatermark(password=password)twm.read_wm(watermark=watermark)twm.read_text(text=text)text_embed=twm.embed()print("打上盲水印之后:")print(text_embed)Then, you can paste this text to where you need.Bob Extract the invisible watermarkfromtext_blind_watermarkimportTextBlindWatermarkpassword="20190808"twm_new=TextBlindWatermark(password=password)wm_extract=twm_new.extract(text_embed)print("解出的盲水印：")print(wm_extract)Related ProjectHideInfo：https://github.com/guofei9987/HideInfo算法说明migrate tank使图片在不同的背景下显示不同的图片hide as image把数据以图片形式存放hide in image把数据藏在一个图片中image seed把图片和文件黏在一起，并存为图片EXIF把一段信息放到图片的EXIF中hide as music把数据以音频的形式存放hide in music把数据隐藏在一个音频中hide as text把数据以文本文件的形式存放hide in text把数据隐藏在一段文本中
Package: textblob
Homepage:https://textblob.readthedocs.io/TextBlobis a Python library for processing textual data. It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more.fromtextblobimportTextBlobtext="""
The titular threat of The Blob has always struck me as the ultimate movie
monster: an insatiably hungry, amoeba-like mass able to penetrate
virtually any safeguard, capable of--as a doomed doctor chillingly
describes it--"assimilating flesh on contact.
Snide comparisons to gelatin be damned, it's a concept with the most
devastating of potential consequences, not unlike the grey goo scenario
proposed by technological theorists fearful of
artificial intelligence run rampant.
"""blob=TextBlob(text)blob.tags# [('The', 'DT'), ('titular', 'JJ'),#  ('threat', 'NN'), ('of', 'IN'), ...]blob.noun_phrases# WordList(['titular threat', 'blob',#            'ultimate movie monster',#            'amoeba-like mass', ...])forsentenceinblob.sentences:print(sentence.sentiment.polarity)# 0.060# -0.341TextBlob stands on the giant shoulders ofNLTKandpattern, and plays nicely with both.FeaturesNoun phrase extractionPart-of-speech taggingSentiment analysisClassification (Naive Bayes, Decision Tree)Tokenization (splitting text into words and sentences)Word and phrase frequenciesParsingn-gramsWord inflection (pluralization and singularization) and lemmatizationSpelling correctionAdd new models or languages through extensionsWordNet integrationGet it now$ pip install -U textblob
$ python -m textblob.download_corporaExamplesSee more examples at theQuickstart guide.DocumentationFull documentation is available athttps://textblob.readthedocs.io/.Project LinksDocs:https://textblob.readthedocs.io/Changelog:https://textblob.readthedocs.io/en/latest/changelog.htmlPyPI:https://pypi.python.org/pypi/TextBlobIssues:https://github.com/sloria/TextBlob/issuesLicenseMIT licensed. See the bundledLICENSEfile for more details.
Package: textblob-aptagger
A fast and accurate part-of-speech tagger based on the Averaged Perceptron. For use withTextBlob.Implementation by Matthew Honnibal, a.k.a.syllog1sm. Read more about ithere.InstallIf you havepipinstalled (you should), run$ pip install -U textblob-aptaggerUsage>>>fromtextblobimportTextBlob>>>fromtextblob_aptaggerimportPerceptronTagger>>>blob=TextBlob("Simple is better than complex.",pos_tagger=PerceptronTagger())>>>blob.tags[('Simple',u'NN'),('is',u'VBZ'),('better',u'JJR'),('than',u'IN'),('complex',u'JJ')]RequirementsPython >= 2.6 or >= 3.3LicenseMIT licensed. See the bundledLICENSEfile for more details.Changelog0.2.0 (10/21/2013)Compatibility with Textblob>=0.8.0.0.1.0 (09/25/2013)First stable release.Ports thePerceptronTaggerfrom TextBlob 0.6.3.0.0.1 (09/22/2013)Experimental release.
Package: textblob-fr
French language support forTextBlob.FeaturesPart-of-speech tagging (PatternTagger)Sentiment analysis (PatternAnalyzer)Supports Python 2 and 3Installing/UpgradingIf you havepipinstalled (you should), run$ pip install -U textblob
$ pip install -U textblob-frUsage>>>fromtextblobimportTextBlob>>>fromtextblob_frimportPatternTagger,PatternAnalyzer>>>text=u"Quelle belle matinée">>>blob=TextBlob(text,pos_tagger=PatternTagger(),analyzer=PatternAnalyzer())>>>blob.tags[(u'Quelle',u'DT'),(u'belle',u'JJ'),(u'matin\xe9e',u'NN')]>>>blob.sentiment(0.8,0.8)Alternatively, you can use theBlobberclass to avoid having to repeatedly pass the models into theTextBlobconstructor.>>>fromtextblobimportBlobber>>>fromtextblob_frimportPatternTagger,PatternAnalyzer>>>tb=Blobber(pos_tagger=PatternTagger(),analyzer=PatternAnalyzer())>>>blob1=tb(u"Quelle belle matinée")>>>blob1.sentiment(0.8,0.8)>>>blob2=tb(u"C'est une voiture terribles.")>>>blob2.sentiment(-0.7,0.6)>>>blob1.analyzerisblob2.analyzerTrueRequirementsPython >= 2.6 or >= 3.3TODOTokenizationParsingNLTK tagging?LicenseMIT licensed. See the bundledLICENSEfile for more details.Changelog0.2.0 (10/27/2013)Compatibility with TextBlob>=0.8.0.0.1.0 (09/25/2013)First releaseBasically a thin, Py3-compatible wrapper around pattern.fr. Hooks to pattern’s tagger and sentiment analyzer.0.0.1 (09/22/2013)Experimental release.
Package: textblob-nl
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the “Software”), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
Description: UNKNOWN
Keywords: textblob,textblob_nl,nlp,linguistics,nltk,pattern
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3