Saravanan666/pypi-org huggingface dataset:
Package: nlmod
nlmodPython package to build, run and visualize MODFLOW 6 groundwater models in the Netherlands.nlmodwas built to allow users to write scripts to quickly download relevant data
from publicly available sources, and build and post-process groundwater flow and
transport models at different spatial and temporal scales to answer specific
geohydrological questions. Scripting these steps, from downloading data to building
groundwater models, makes models more reproducible and transparent.The functions innlmodhave four main objectives:Create and adapt the temporal and spatial discretization of a MODFLOW model using an xarray Dataset (nlmod.dims).Download and read data from external sources, project this data on the modelgrid and add this data to an xarray Dataset (nlmod.read).Use data in an xarray Dataset to build modflow packages for both groundwater flow and transport models using FloPy (nlmod.sim,nlmod.gwfandnlmod.gwtfor Modflow 6 andnlmod.modpathfor Modpath).Visualise modeldata in Python (nlmod.plot) or GIS software (nlmod.gis).More information can be found on the documentation-website:https://nlmod.readthedocs.io/.InstallationInstall the module with pip:pip install nlmodnlmodhas the following required dependencies:flopyxarraynetcdf4rasteriorioxarrayaffinegeopandasowslibhydropandasshapelypyshprtreematplotlibdaskcoloramajoblibThere are some optional dependecies, only needed (and imported) in a single method.
Examples of this arebottleneck(used in calculate_gxg),geocube(used in
add_min_ahn_to_gdf),h5netcdf(used for hdf5 files backend in xarray),scikit-image(used in calculate_sea_coverage). To installnlmodwith the optional dependencies use:pip install nlmod[full]When using pip the dependencies are automatically installed. Some dependencies are
notoriously hard to install on certain platforms. Please see thedependenciessection of thehydropandaspackage for more information on how to install these packages manually.Getting startedIf you are usingnlmodfor the first time you need to download the MODFLOW
executables. You can easily download these executables by running this Python code:import nlmod
nlmod.download_mfbinaries()After you've downloaded the executables you can run the Jupyter Notebooks in the
examples folder. These notebooks illustrate how to use thenlmodpackage.
Package: nlnormaliz
# nlnormaliz.py[![PyPI version](https://badge.fury.io/py/nlnormaliz.svg)](https://badge.fury.io/py/nlnormaliz)
[![Python versions](https://img.shields.io/pypi/pyversions/nlnormaliz.svg)](setup.py)
[![Build Status](https://travis-ci.org/raviqqe/nlnormaliz.py.svg?branch=master)](https://travis-ci.org/raviqqe/nlnormaliz.py)
[![License](https://img.shields.io/badge/license-unlicense-lightgray.svg)](https://unlicense.org)Natural language normalizer mainly for English and Japanese documents in Python## License[The Unlicense](https://unlicense.org)
Package: nlogger
NLoggerThis is package to send notifications to SmartPhones 
which is installed with NLogger and an account registered in it.
This package serves as a wrapper to send notifications to the registered devices.
Thanks for using NLogger.For any help, feedback, or to report an error -> mail: bala21karthick@gmail.comNLoggerReleases07-05-2020 - 1_beta - initial beta testing
Package: nlohmann-json
nlohmann-json PyPIThis repo contains automation fornlohmann-json.It creates, packages and pushes the header-only library to PyPI.
This repo is the equivalent of theAnaconda nlohmann_jsonpackage.NOTE:Packages created by this repo only contain the raw source code and not the Python package.Credits go to the maintainers of thenlohmann-jsonpackage.LicenseThe files in this repository are licensed under theMIT License.The distributed nlohmann-json package is a redistribution ofnlohmann-jsonand therefore falls under thenlohmann-json license modelas described in theLICENSE.MIT file in the nlohmann-json.
Package: nlon-py
nlon-pyPython package for identifying text containing natural language (or not) using machine learning.Free software: MIT licenseDocumentation:https://nlon-py.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.4 (2021-07-18)Implement basic model and functions0.1.0 (2021-06-10)First release on PyPI.
Package: nlp
Simple check list for release from AllenNLP repo:https://github.com/allenai/allennlp/blob/master/setup.pyTo create the package for pypi.Change the version in __init__.py, setup.py as well as docs/source/conf.py.Commit these changes with the message: “Release: VERSION”Add a tag in git to mark the release: “git tag VERSION -m’Adds tag VERSION for pypi’ ”
Push the tag to git: git push –tags origin masterBuild both the sources and the wheel. Do not change anything in setup.py between
creating the wheel and the source distribution (obviously).For the wheel, run: “python setup.py bdist_wheel” in the top level directory.
(this will build a wheel for the python version you use to build it).For the sources, run: “python setup.py sdist”
You should now have a /dist directory with both .whl and .tar.gz source versions.Check that everything looks correct by uploading the package to the pypi test server:twine upload dist/* -r pypitest
(pypi suggest using twine as other methods upload files via plaintext.)
You may have to specify the repository url, use the following command then:
twine upload dist/* -r pypitest –repository-url=https://test.pypi.org/legacy/Check that you can install it in a virtualenv by running:
pip install -ihttps://testpypi.python.org/pypinlpUpload the final version to actual pypi:
twine upload dist/* -r pypiCopy the release notes from RELEASE.md to the tag in github once everything is looking hunky-dory.Update the documentation commit in .circleci/deploy.sh for the accurate documentation to be displayedUpdate README.md to redirect to correct documentation.
Package: nlp2cron
中文自然语言转CRONpip install nlp2cronfromnlp2cronimportnlp2croncron=nlp2cron("每天10点25分叫我去买菜")你会得到一个cron表达式. 目前代码写的有点乱, 但只要不太随意的自然语言都可以解决
代码里面有一些例子, 详细看代码啦. 代码写的比较随意, 如果要求高请不要用啦.
Package: nlp2fn
Python Automation Made Simple:nlp2fnWelcome tonlp2fn, a seamless and intuitive software tool for Python automation. With the ability to load and read Python functions from any specified location,nlp2fnbrings all your automation tasks together. Whether your functions reside in local directories or remote servers,nlp2fnfetches, loads, and prepares them for execution — as simple as a statement.The Magic of nlp2fnA typical function fornlp2fnfollows this format:# Write you execute statement, this shall be an# input by user to execute your events, make sure all the# parameters that are required by event is properly captured with {param_name}.STATEMENT="download{link}to{output_dir}"# This shall be the main function for your event. oncedefexecute(args):file=args[0]output_dir=args[1]# Complete the function.returnTrueTheexecutefunction is the heart of the operation where the actual task takes place.Quick Start GuideHere's how to install and get started withnlp2fn:pipinstallnlp2fnNext, add the source directory where your Python functions are located:nlp2fnadd-source/path/to/directoryornlp2fnadd-sourcehttps://github.com/{githubid}/{repo}make sure repo is public.Ready to launchnlp2fnas a chatbot? Use this command:nlp2fnrunTo run a single statement, use theexeccommand:nlp2fnexec-m"statement"If you need to reset your sources, simply use theresetcommand:nlp2fnresetIf you need to remove any source your sources, simply use thedeletecommand:nlp2fndeleteIf you need to update the source, both remote or local useupdatecommandnlp2fnupdateJoin the DevelopmentWe welcome your contributions! Feel free to submit pull requests and create issues on ourGitHub page.View ChangeLogGitHub Release page.ContactFor questions, suggestions, or any kind of discussion, feel free to open an issue on our GitHub page.Embrace the simplification of Python automation withnlp2fn.EventsLangchain Automations :https://github.com/dextrop/evt-langchainhttps://medium.com/@askjennie/creating-your-own-automation-library-with-nlp2fn-6657b1276361
Package: nlp2go
nlp2goHosting nlp models in one lineDemodemo websiteIntroductionOnce the model is trained, we want to verify our model as quickly as possible.
nlp2go provides a CLI interface and  Restful api that allows you to quickly deploy model to everyone.FeatureThere are many additional features :Support loading multiple models at a time.Provide input format checking.You can also load models in python code.Flexible handling of parameters so that parameter can be changed in each prediction.Support huggingface transformers’s modelThere are models in the Model Hub for you to tryDocumentationLearn more from thedocs.Quick StartInstalling via pippipinstallnlp2gohosting single modelnlp2go --model model_pathhosting multiple modelscreate a json file as below:{"API1_PATH":{"model":"model1_path"},"API2_PATH":{"model":"model2_path"}}runnlp2go --json json_file_pathYou can also try nlp2go in Google Colab:ContributingThanks for your interest.There are many ways to contribute to this project. Get startedhere.LicenseLicenseIcons referenceIcons modify fromGood Warefromwww.flaticon.comIcons modify fromFreepikfromwww.flaticon.com
Package: nlp-900
NLP T9Pre-requispython 3.7InstallationTo install nlp-900, simply use pip:pipinstallnlp-900To install nlp-900, on git:gitclonehttps://gitlab.com/red-56/t9-nlp.gitcd./t9-nlp
pythonsetup.pyinstall
nlp-900--helpHelp CliOptionOptionTypeExamplehelp-o, --outputraw,stdout,voicerawType d'ouput voulu, raw pour un fichier audio, voice pour un message automatique, et stdout pour des print.(Default = stdout)-i, --inputraw,stdin,voicerawType d'input voulu, raw pour des fichiers audio,voice pour le micro, et stdin pour une phrase prise en argument.(Default = stdin)-f, --filePATH./sample/paris_nevers.wavChemin du fichier audio-e, --escaleINTEGER > 03Nombre d'escales maximum à ne pas dépasser.(Default = 2)-vv, --verboseFLAGAffiche les messages de debug.--versionFLAGShow the version and exit.--helpFLAGShow the help message and exit.ExamplesTables of contentsBasic UsagePhrase de la forme Gare d'arrivée Gare de départPhrase longueDemande d'escaleTrajet avec trop d'escalesTrajet introuvableGare IntrouvalePrendre en input un fichier audioPrendre en input son microRécuperer l'output dans un fichier audioRécuperer l'output sur la sortie audio standardAugmenter le nombre maximum d'escaleAfficher le mode debugBasic examplenlp-900"un billet pour paris-nevers s'il vous plait"output:LetempsdetrajetpourvotrevoyagepourNeversenpartantdeParis-austerlitzserade230minutes.VousdevrezpasserparBourges.Phrase de la forme Gare d'arrivée Gare de départnlp-900"je veux aller à Paris, je suis à Rouen"output:LetempsdetrajetpourvotrevoyagepourParis-st-lazareenpartantdeRouen-rive-droiteserade533minutes.VousdevrezpasserparRomans-bourg-de-péage,Caen.Phrase longuenlp-900"j'aime les pommes, j'ai fêté noël hier je veux rentrer à Paris, ma maman m'a fait des sandwitchs et je suis actuellement à Rouen."output:LetempsdetrajetpourvotrevoyagepourParis-st-lazareenpartantdeRouen-rive-droiteserade533minutes.VousdevrezpasserparRomans-bourg-de-péage,Caen.Demande d'escalenlp-900"un billet pour paris-caen en passant par rouen s'il vous plait"output:LetempsdetrajetpourvotrevoyagepourCaenenpartantdeParis-st-lazareserade481minutes.VousdevrezpasserparRouen-rive-droite,Romans-bourg-de-péage.Trajet avec trop d'escalesnlp-900"je voudrais aller à nevers, je suis à paris, et je dois passer par Rouen pour voir ma tante."output:Trajettrouvémaisilcomporte7escales,pourl'afficher ajouter l'option--escale7.Trajet introuvablenlp-900"je souhaite me rendre à Bourges, en partance de Argentan"output:TrajetintrouvableGare introuvablenlp-900"je pars de epitech je veux rentrer chez moi."output:GareIntrouvable!!!
Format:jeveuxallerde(GareDépart)à(Gared'arrivée).
ex:jeveuxallerdebourgesàneversPrendre en input un fichier audionlp-900--inputraw-f./sample/lille_nevers.wavPrendre en input son micronlp-900--inputvoiceRécupérer l'ouput dans un fichier audionlp-900--outputrawLe fichier sera nommé output.wavRécupérer output sur la sortie audio standardnlp-900--outputvoiceAugmenter le nombre d'escales maximumnlp-900--escale7"je voudrais aller à nevers, je suis à paris, et je dois passer par Rouen pour voir ma tante."Afficher le mode debugnlp-900-vv
Package: nlpa
NLPA =  Natural Language Processing AutomationNLPA is python package which build with help of Pandas and NLTK libray.
NLPA integrate the some methods of the above mentioned libray to ease the task of cleaning
the text data.Cleaning text data in which we have to perform the some basic task in repetition
like removing stowpword, word tokenizing ,lemmatizing , lowering text and etc…
all these functionality can be achived in one single call.In this project we simply call the fucntions  cleanData and passing parameter into it and it return the
clean text or we can call each individual fucntions and perform our cleaning as per our need.Change Log0.1.0 (First Release)Initial Release8-April,2021
Package: nlp-annotations
NLP AnnotationsA simple python package for dealing with different nlp annotation styles.
No dependencies, and is very fast due to only using regular expressions.You can install it with:pip3installnlp-annotationsAnnotation TypesThe following are the annotation types we support and how to convert it to
another type.Markdown Links(Used by Rasa, etc...), these are in the form:The weather is [sunny](weather) and the sky is [blue](color).To convert this to anentity listyou can:fromnlp_annotationsimportmarkdown_links2entity_listmarkdown_links2entity_list("The weather is [sunny](weather) and the sky is [blue](color).")# ('The weather is sunny and the sky is blue.', {'entities': [(15, 20, 'weather'), (36, 40, 'color')]})For other situations that you may wish to add extra logic, there is also a generator:fromnlp_annotationsimportmarkdown_string_link_generatorforlinkinmarkdown_string_link_generator("The weather is [sunny](weather) and the sky is [blue](color)."):print(f"- word={link.word}, entity={link.entity}, start={link.start}, end={link.end}")# - word=sunny, entity=weather, start=15, end=31# - word=blue, entity=color, start=47, end=60Entity List(Used by Spacy, etc...), these are in the form:('The weather is sunny and the sky is blue.',{'entities':[(15,20,'weather'),(36,40,'color')]})To convert this to amarkdown linksstring, you can:fromnlp_annotationsimportentity_list2markdown_linksentity_list2markdown_links("The weather is sunny and the sky is blue.",[(15,20,'weather'),(36,40,'color')])# 'The weather is [sunny](weather) and the sky is [blue](color).'
Package: nlpatl
NLPatl (NLP Active Learning)This python library helps you to perform Active Learning in NLP. NLPatl built on top of transformers, scikit-learn and other machine learning package. It can be applied into both cold start scenario (no any labeled data) and limited labeled data scenario.The goal of NLPatl is to make use of the state-of-the-art (SOTA) NLP models to estimate the most valueable data and making use of subject matter experts (SMEs) by having them to label limited amount data.At the beginning, you have unlabeled (and limited labeled data) only. NLPatl apply transfer learning to convert your texts into vectors (or embeddings). After that, vectors go through unsupervised learning or supervised learning to estimate the most uncertainty (or valuable) data. SMEs perform label on it and feedback to models until accumulated enough high quailty data.Installationpip install nlpatlorpip install git+https://github.com/makcedward/nlpatl.gitExamplesQuick tour for text inputQuick tour for image inputCustom Embeddings, Classification, Clustering and Learning functionRelease0.0.2, Dec 17, 2021[Completed] Transformers supports Tensorflow[Completed] Performance tuning during clustering[Completed] Support multi-label[Completed] Custom Embeddings, Classification, Clustering, Scoring(Learning) function[Completed] Support TorchVision for image embeddings[Completed] Support SentenceTransformers[Completed] Add Least Confidence Sampling and Most Confidence Sampling[Completed] Add Semi-supervised learning[Completed] Add Farthest (Clustering) Sampling, Mismatch (Uncertainity) Sampling[Completed] Add Mismatch-farthest LearningCitation@misc{ma2021nlpatl,
  title={Active Learning for NLP},
  author={Edward Ma},
  howpublished={https://github.com/makcedward/nlpatl},
  year={2021}}
Package: nlp-augment
nlp-augmentIt takes an sentence as an input and augment it based on sense, pos tag synonym, and synonyms.Installationpip install nlp-augmentFor jupyter Notebook!pip install nlp-augmentHow to use it?import augmentMethod 1:We applied word-sense disambiguation to each word of the input sentence, after the preprocessing stagethat removes stopwords and other uncommon characters. The synonymy relation was used to extract the list of sensesfor each word. Next, to find out which of these senses better fit the context of the sentence, Leskâ€™s algorithm wasemployed. The original version of this algorithm disambiguates words in short sentences. For that, the gloss ofthe word to disambiguate (dictionary of its senses) is compared to glosses of other words of the sentence. Then, thesense that shares the most significant number of common words with the glosses of other words of the phrase is chosenand assigned to the target word.Exmaple of hate sentence aumentationaugment.sense('you are gay')['you are gay', 'you are queer', 'you are homophile']Method 2:We apply a Part of Speech (PoS) Tagging to each sentence, which is later used to extract all meanings(synsets) and synonyms that correspond to that word #PoS combination. This approach could widely expand thesemantic space over the previously mentioned data augmentation approach (method 1), as one word could have multiplemeanings of the same part of speech.>>> augment.pos('you are gay')['you are gay', 'you are cheery', 'you are sunny', 'you are jocund', 'you are jolly', 'you are jovial', 'you are merry', 'you are mirthful', 'you are brave', 'you are braw', 'you are festal', 'you are festive', 'you are queer', 'you are homophile']Method 3:We extract all possible meanings (synsets) of every complete word (after preprocessing), and then weretrieve the synonyms associated with every possible meaning. This significantly expands the semantic space of eachsentence compared to the first two methods. We are considering here all possible meanings (including every PoS thatthis word may belong to) as well as the similar words of each meaning regardless of the coherence of the correspondingcontext.augment.synonym('you are gay')['you are gay', 'you ar gay', 'you be gay', 'you exist gay', 'you equal gay', 'you constitute gay', 'you represent gay', 'you make up gay', 'you comprise gay', 'you follow gay', 'you embody gay', 'you personify gay', 'you live gay', 'you cost gay', 'you are homosexual', 'you are homophile', 'you are homo', 'you are cheery', 'you are sunny', 'you are jocund', 'you are jolly', 'you are jovial', 'you are merry', 'you are mirthful', 'you are brave', 'you are braw', 'you are festal', 'you are festive', 'you are queer']LicenseÂ© 2021 Md Saroar JahanThis repository is licensed under the MIT license. See LICENSE for details.
Package: nlpbaselines
nlpbaselinesEstablish quick baselines for NLP tasks.Citation:@Manual{url,
  author={Wang Xiaoou},
  title={nlpbaselines: establish quick baselines for NLP tasks},
  year={2022},
  url={https://github.com/xiaoouwang/nlpbaselines},
}How to use the packageTrain a Camembert classifier on a dataset without cross validationEvaluate Camembert, Flaubert and DistilCamemBERT on a sentiment analysis datasetmore comingLicenseCodesnlpbaselinesis licensed under Apache License 2.0. You can usenlpbaselinesin your commercial products for free. We would appreciate it if you add a link tonlpbaselineson your website.ModelsUnless otherwise specified, all models innlpbaselinesare licensed underCC BY-NC-SA 4.0.
Package: nlp-basictasks
nlp-basictasksnlp-basictasks是利用PyTorch深度学习框架所构建一个简单的库，旨在快速搭建模型完成一些基础的NLP任务，如分类、匹配、序列标注、语义相似度计算等。整体架构如上图，整体架构分为三个大模块：readers负责读取数据，不同的任务在readers中有不同的数据处理形式。不同任务的数据后处理代码也放在readers中。modules代表编码器，用来提取文本特征，主要包括BERT、LSTM、MLP。tasks根据任务的不同有相应的处理方式。（和readers中相应的数据处理方式对应）安装pipinstall--index-urlhttps://pypi.org/project/nlp-basictasks==0.2.3orgitclonehttps://github.com/xianghuisun/nlp-basictasks.gitcdnlp-basictasks&pythonsetup.pyinstall使用notebooks中展示了用nlp-basictasks框架快速实现五个NLP常见任务，具体见：实体识别语义相似度计算文本分类文本匹配问答检索参考PALMpytorch-crfFaisssentence-transformerstransformers
Package: nlpboss
作为一个经常使用transformers包的工程师作为一个不能远程、懒得远程的，但是有时候需要了解transformers训练的情况的工程师我发布了这个包，用来解决远程无法查看模型训练效果的问题。使用起来很简单，建议直接查看GitHub上的介绍：https://github.com/yuanzhoulvpi2017/nlpboss
Package: nlpc
nlpcnlpcis a super fast c++ library which adopts dynamic programming(DP) algorithm to solve classic nlp problems as below .The longest common subsequenceproblem is the problem of finding the longest subsequence common to all sequences in a set of sequences (often just two sequences).The longest common substringproblem is to find the longest string (or strings) that is a substring (or are substrings) of two or more strings.We also support Chinese(or any UTF-8) stringInstallTo install, simply dopip install nlpcto pull down the latest version fromPyPI.Python code exampleimportnlpc#  finding the longest common subsequence length of string A and string BA='We are shannonai'B='We like shannonai'nlpc.lcs(A,B)""">>> nlpc.lcs(A, B)14"""#  finding the longest common subsequence length of string A and a list of string BA='We are shannonai'B=['We like shannonai','We work in shannonai','We are not shannonai']nlpc.lcs_of_list(A,B)""">>> nlpc.lcs_of_list(A, B)[14, 14, 16]"""# finding the longest common substring length of string A and string BA='We are shannonai'B='We like shannonai'nlpc.lcs2(A,B)""">>> nlpc.lcs2(A, B)11"""#  finding the longest common substring length of string A and a list of string BA='We are shannonai'B=['We like shannonai','We work in shannonai','We are not shannonai']nlpc.lcs2_of_list(A,B)""">>> nlpc.lcs2_of_list(A, B)[11, 10, 10]"""
Package: nlp-cache-sebastian-stigler
NLP CacheA caching component forDocclasses inspacy.Installationpipinstallnlp_cache_sebastian_stiglerUsageimportspacynlp=spacy.load("de_core_news_sm")text="Ich wünsche Ihnen einen guten Morgen!"doc_1=nlp(text)# mean loading time about 4.5 msdoc_2=nlp(text)# recurrent mean loading time about 4.5 msTo speed up loading a recurrent text you can use this caching utility, to
lookup known texts from the cache stored in thecachedirectory (relative
to the current directory).importspacyfromnlp_cacheimportNlpCachenlp=NlpCache(uncached_nlp=spacy.load("de_core_news_sm"),path="cache")text="Ich wünsche Ihnen einen guten Morgen!"doc_1=nlp(text)# mean loading time about 4.5 msdoc_2=nlp(text)# recurrent mean loading time about 840 µs
Package: nlpcleaner
NlpcleanerClean and prepare text for modeling with machine learning.lower allstrip allremove numbersremove symbolsremove urlstrip html tagsremove stopwords by detected language or passed languagelemming or stemmingUsagefrom nlpcleaner import TextCleaner
TextCleaner(txt).clean()Testspipenv install .
python setup.py testPush on PyPipython setup.py sdist
pip install twine
twine upload dist/*TODOAdd tests to cover different cases and languages;check performances
Package: nlpcloud
NLP Cloud serves high performance pre-trained or custom models for NER, sentiment-analysis, classification, summarization, paraphrasing, grammar and spelling correction, keywords and keyphrases extraction, chatbot, product description and ad generation, intent classification, text generation, image generation, code generation, question answering, automatic speech recognition, machine translation, language detection, semantic search, semantic similarity, speech synthesis, tokenization, POS tagging, embeddings, and dependency parsing. It is ready for production, served through a REST API.This is the Python client for the API.More details here:https://nlpcloud.comDocumentation:https://docs.nlpcloud.comGithub:https://github.com/nlpcloud/nlpcloud-python