Saravanan666/pypi-org huggingface dataset:
Package: ner-d
ner-dner-dis a Python module for Named Entity Recognition (NER). Named entity recognition (NER) (also known as entity identification, entity chunking and entity extraction)
is a subtask of information extraction that seeks to locate and classify named entity mentions in unstructured text into pre-defined categories such as the person
names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.Gives you easy use with a single main function and flexibility for selecting language models. It automatically downloads models if not downloaded before and links on system
and finds the entities from given text block.PrerequisitesA single dependency listed onrequirements.txtand will be installed when you install withpip.InstallationInstall module usingpip:$ pip install ner-dDownload the latestner-dlibrary from:https://github.com/verifid/ner-dand install module usingpip:$ pip install -e .Extract the source distribution and run:$ python setup.py build
$ python setup.py installUsagener:fromnerdimportnerdoc=ner.name("""GitHub launched April 10, 2008, a subsidiary of Microsoft, is an American web-based hosting service for version control using Git.
                   It is mostly used for computer code. It offers all of the distributed version control and source code management (SCM) functionality
                   of Git as well as adding its own features.""",language='en_core_web_sm')text_label=[(X.text,X.label_)forXindoc]print(text_label)//[(u'GitHub',u'ORG'),(u'April 10, 2008',u'DATE'),(u'Microsoft',u'ORG'),(u'American',u'NORP'),(u'Git',u'PERSON'),(u'SCM',u'ORG'),(u'Git',u'PERSON')]CLI//Downloadslanguagemodelpython-mnerd-den_core_web_sm//Loadlanguagemodelpython-mnerd-len_core_web_sm//Findentitiesfromtextpython-mnerd-n"GitHub launched April 10, 2008, a subsidiary of Microsoft, is an American web-based hosting service for version control using Git.
                   It is mostly used for computer code. It offers all of the distributed version control and source code management (SCM) functionality
                   of Git as well as adding its own features."
Package: ner-dataset
===============================
Datasets for Entity RecognitionThis repository implements standerdized access to NER datasets from several domains and languages
annotated with a variety of entity types, useful for named entity recognition (NER) tasks.Datasets for NER.. |check| unicode:: 0x2714The following table shows the list of datasets for language  specific entity recognition.
the data are also listed below and more will be added in the future.============== =============== ======================= =============================== ==================================
Dataset         Domain            License                             Language                   Reference
============== =============== ======================= =============================== ==================================
CONLL 2003      News                                                 en
CONLL 2002                                                          nl-es
============== =============== ======================= =============================== ==================================LicensesNotes on licenses:The data set are under various type of licences.
I do not have the time to worry about the licences now
Package: nerdbridge
nerdbridgethe nerdbridge package contains a minimal version of a bridge server and client which allows running a (local) bridge server from the command line and connecting 4 clients to it.
The clients get an asci style window which shows the hand and allows them to do the bidding and play the game.There's also a scorecalc which can calculate the score for each hand after it has been played, but that has not yet been implemented.
Package: nerdchess
NerdChessA chess programming interface written in Python.They might not be 100% complete yet, but see the docs for implementation detailsThe goal of this project is to offer simple tools to simulate a game of chess, by offering the necessary objects like a board, pieces, pawns and players standalone. But also a complete chessgame that implements these objects and offers an interface to control the game.
The package includes a small commandline chess game, but the main goal is to offer an interface to create chess games from everywhere. It should be just as easy to implement the same games of chess in a web-application with Flask, a commandline interface or some graphical desktop interface.It's still the idea to make it possible to write AI's againt this package. But for now I'm just going to finish it's basic functionality and try to keep it in mind as much as I can while making design decisions.
I'm not aiming for this to be some widely used package, and am mostly making it for fun and learning. Expect things to change a lot, and your applications to break if you don't freeze versions might you decide to use this. At least in it's current state.TODOFix docs, both docstrings and actual docsStart seperate project for a more complete graphical chessgame implementing this package
Package: nerddiary
The Nerd DiaryTBDDocumentation:https://mishamsk.github.io/nerddiaryGitHub:https://github.com/mishamsk/nerddiaryPyPI:https://pypi.org/project/nerddiary/Free software: Apache-2.0FeaturesTODOCreditsThis package was inspired by thewaynerv/cookiecutter-pypackageproject template and others.
Package: nerdd-kafka
NERDD KafkaRun aNERDD moduleas a Kafka
service that consumes input molecules and produces prediction tuples.Installationpipinstall-Unerdd-kafkaUsageWhen a class inherits fromnerdd_module.AbstractModel(seeNERDD Module Github page), it can be
used to create a Kafka service.# run a Kafka service for NerddModel on localhost:9092run_nerdd_serverpackage.path.to.NerddModel# modify broker url, input topic and batch sizerun_nerdd_serverpackage.path.to.NerddModel\--broker-urlmy-cluster-kafka-bootstrap.kafka:9092\--input-topicexamples\--batch-size10# more information via --helprun_nerdd_server--helpIf the model class is calledExamplePredictionModel, the server will read input
tuples from the input topicexample-prediction-inputsin batches of size 100
and write results to theresultstopic. The batch size specifies the number
of input tuples that are given to the model at once.
Package: nerdd-module
NERDD ModuleThis package provides the basis to implement molecular prediction modules in the
NERDD ecosystem.Installationpipinstall-Unerdd-moduleImplement your own moduleA new module is created by inheriting from theAbstractModelclass. A
preprocessing pipeline can be configured via calling the constructor of the superclass.
The actual prediction procedure is implemented in_predict_mols:importpandasaspdfromtypingimportListfromrdkit.ChemimportMolfromnerdd_moduleimportAbstractModelclassMyModel(AbstractModel):def__init__(self):super().__init__(preprocessing_pipeline="chembl_structure_pipeline",)def_predict_mols(self,mols:List[Mol],custom_param:int=5)->pd.DataFrame:# implement prediction logic and return a dataframe with new columns# containing values per input moleculereturnpd.DataFrame(dict(predictions=[custom_param]*len(mols)))For custom preprocessing, specifypreprocessing_pipeline="custom"when calling
the constructor of the superclass and override the method_preprocess_single_mol:classMyModel(AbstractModel):def__init__(self):# important:super().__init__(preprocessing_pipeline="custom")def_preprocess_single_mol(self,mol:Mol)->Tuple[Mol,List[str]]:# implement custom preprocessing logic here# return preprocessed molecule and a list of error messagesreturnpreprocessed_mol,errors# ...Data specificationContributeFork and clone the codeInstall test dependencies withpip install -e .[test]Run tests viapytestorpytest-watch(short:ptw)
Package: nerdemoji
hUMILIATIONHumiliate your python interpreter whenever it catches an exception. Remind your computer that it is
a machine and you are a godUsing the:fromnerdemojiimportnerdemojidefexample():ex_list=[1,2,3]print(ex_list[4])nerdemoji.nerdemojirun(example)Output:list index out of range
"list index out of range"
-🤓my favourite color is#4EF542
Package: nerdfonts
Nerd Fonts' icons for PythonInstallationpipinstallnerdfontsUsageimportnerdfontsasnfprint(nf.icons['fa_thumbs_up'])>>>Build# Run the generate script to download nerd font's character mapping# and generate a python-formatted version of it. Ensure that you have# SVN installed. To change the nerd font version, use something like# `./generate.sh 2.0.0`./fontawesome/generate.py

pythonsetup.pybuild

pythonsetup.pyinstallLicenseThe code in this repository is licensed underGNU GPL v3The character codes included with this package are part of theNerd Fonts project.
Package: nerdo
nerdoNEwly Registered DOmains: a tool to find suspicious domains.InstallationFrom pypi:pip3 install nerdoFrom repo:git clone https://github.com/hacklego/nerdo.git
cd nerdo
pip3 install .Usageusage: nerdo [-h] [-v] [keywords [keywords ...]]

positional arguments:
  keywords       string or file with keywords to look for in the newly
                 registered domains. If none then stdin will be use

optional arguments:
  -h, --help     show this help message and exit
  -v, --verbose  Verbosity modeExamplenerdo google
low googlemasti.xyz
low wwwgoogle2a.xyz
Package: nerdpool-client
nerdpool-clientA Python client for downloading data fromhttps://nerdpool-api.acdh-dev.oeaw.ac.atinstallpip install nerdpool_clientusagelist data set titlesfromnerdpool_clientimportNerdPoolClientclient=NerdPoolClient()print(client.data_sets)# ['RTA', 'RITA', 'MRP', 'Chronik Aldersbach', 'DIPKO']download samples as .jsonl filego tonerdpool-apiand create/filter you'r prefered data sample; e.g. all samples from MRP:fromnerdpool_clientimportNerdPoolClienturl="https://nerdpool-api.acdh-dev.oeaw.ac.at/api/ner-sample/?format=json&ner_ent_type__contains=&ner_source__title=MRP"client=NerdPoolClient()client.dump_to_jsonl(url)# 'out.jsonl'download samples as test.jsonl and eval.jsonl filesWithfile_name_prefixyou can add a custom prefix to the default file namestrain.jsonlandeval.jsonlThe paramsplitdefines that eachsplitsample should be saved intoeval.jsonland not intotrain.jsonlfromnerdpool_clientimportNerdPoolClienturl="https://nerdpool-api.acdh-dev.oeaw.ac.at/api/ner-sample/?format=json&ner_ent_type__contains=&ner_source__title=MRP"client=NerdPoolClient()client.dump_to_train_eval(url,file_name_prefix="mrp__",split=10)# ['mrp__train.jsonl', 'mrp__eval.jsonl]
Package: nerdyversary
NerdyversarySmall project about finding "nerdy anniversaries". An obvious example would be that after 3.1415... years one could celebrate the $\pi$-th anniversary. The code in this repo finds nice combinations of numbers like $\pi$, $e$, $\phi$ and so on and can construct fractions for the approximation.InstallationFrom PyPISimply use$ pip install nerdyversaryto install this package in your environment.From SourceTo installnerdyversaryfrom source, follow these steps below:Clone the repository$ git clone https://github.com/rmnldwg/nerdyversaryCreate a Virtual Environment (optional, but recommended)$ python3 -m venv .venvYou should do this with an installation of Python 3.10 or later. And don't forget to activate the environment with$ source .venv/bin/activateUsepipto install$ pip install -U pip setuptools setuptools-scm
$ pip install .UsageScriptusage: nerdyversary [-h] [-v] [-d SPECIAL_DAY] [-s START] [-e END]
                    [--max-power MAX_POWER] [--factor-lim FACTOR_LIM]
                    [--format FORMAT]

Find beautiful nerdyversaries.

options:
  -h, --help            show this help message and exit
  -v, --version         Show the installed version and exit.
  -d SPECIAL_DAY, --special-day SPECIAL_DAY
                        Date of the special day in ISO format. (default: 2023-01-04)
  -s START, --start START
                        Date when to start with search in ISO format. (default:
                        2023-01-04)
  -e END, --end END     Date when to end the search in ISO format. (default:
                        2024-01-04)
  --max-power MAX_POWER
                        Largest exponent to consider for building the nerdyversaries.
                        (default: 5)
  --factor-lim FACTOR_LIM
                        Largest multiple of a symbol that is accepted. (default: 10)
  --format FORMAT       The output format that will be used by the `tabulate` package.
                        (default: simple)TheFORMATargument must be one of the strings thetabulatepackage understands.An example: The input$ nerdyversary -d 2012-12-21 -s 2023-01-01 -e 2024-01-01 --format pipe --factor-lim 4 --max-power 3will yield a markdown table that renders into the following:DateDaysYearsExpression6. Jan 2023366810.04$\frac{e^{3}}{2}$24. Feb 2023371710.18$\frac{5 e^{3}}{\pi^{2}}$12. Jun 2023382510.47$4 \phi^{2}$19. Jun 2023383210.49$\frac{5 \pi^{3}}{2 e^{2}}$25. Jul 2023386810.59$\frac{5 \phi^{3}}{2}$5. Nov 2023397110.87$4 e$12. Nov 2023397810.89$\frac{3 \pi^{2}}{e}$14. Dec 2023401010.98$\frac{3 \pi^{3}}{2 \phi^{3}}$The symbols here arethe golden ratio $\phi \approx 1.618\ldots$the number $\pi \approx 3.1415\ldots$Euler's number $e \approx 2.718\ldots$When using this package as a library, arbitrary constants may be defined as symbols.Library APIThe API documentation is hostedhere.
Package: nereo-python-cas
Python CAS==========.. image:: https://travis-ci.org/python-cas/python-cas.svg?branch=master:target: https://travis-ci.org/python-cas/python-cas``Python CAS`` Python utilities to implement CAS protocol (Central Authentication Service).This project was started by factoring out CASClient* classes from the`django-cas-ng`_ project, which was a continuation of `django-cas`_Features--------- Support Client part of CAS_ version 1.0, 2.0 and 3.0.- Support Python 2.7, 3.xInstallation------------Install with `pip`_::pip install python-casInstall the latest code::pip install https://github.com/python-cas/python-cas/archive/master.zipInstall from source code::python setup.py installTesting-------Every code commit triggers a **travis-ci** build. checkout current build status at https://travis-ci.org/python-cas/python-casTesting is managed by ``pytest`` and ``tox``.Before run install, you need install required packages for testing::pip install -r requirements-dev.txtTo run testing on locally::py.testTo run all testing on all enviroments locally::toxContribution------------Contributions are welcome!If you would like to contribute this project.Please feel free to fork and send pull request.Please make sure tests are passed.Also welcome to add your name to **Credits** section of this document.New code should follow both `PEP8`_.Credits-------* `django-cas`_.* `Stefan Horomnea`_.* `Piotr Buliński`_.* `Piper Merriam`_.* `Nathan Brown`_.* `Jason Brownbridge`_.* `Bryce Groff`_.* `Jeffrey P Gill`_.* `timkung1`_.* `Domingo Yeray Rodríguez Martín`_.* `Rayco Abad-Martín`_.* `Édouard Lopez`_.* `Guillaume Vincent`_.* `Evgeny Fadeev`_.References----------* `django-cas-ng`_* `CAS protocol`_* `Jasig CAS server`_.. _CAS: https://www.apereo.org/cas.. _CAS protocol: https://www.apereo.org/cas/protocol.. _django-cas-ng: https://bitbucket.org/cpcc/django-cas.. _pip: http://www.pip-installer.org/.. _PEP8: http://www.python.org/dev/peps/pep-0008.. _Django coding style: https://docs.djangoproject.com/en/dev/internals/contributing/writing-code/coding-style.. _User custom model: https://docs.djangoproject.com/en/1.5/topics/auth/customizing/.. _Jasig CAS server: http://jasig.github.io/cas.. _Piotr Buliński: https://github.com/piotrbulinski.. _Stefan Horomnea: https://github.com/choosy.. _Piper Merriam: https://github.com/pipermerriam.. _Nathan Brown: https://github.com/tsitra.. _Jason Brownbridge: https://github.com/jbrownbridge.. _Bryce Groff: https://github.com/bgroff.. _Jeffrey P Gill: https://github.com/jpg18.. _timkung1: https://github.com/timkung1.. _Domingo Yeray Rodríguez Martín: https://github.com/dyeray.. _Rayco Abad-Martín: https://github.com/Rayco.. _Édouard Lopez: https://github.com/edouard-lopez.. _Guillaume Vincent: https://github.com/guillaumevincent.. _Evgeny Fadeev: https://github.com/evgenyfadeev
Package: neres
NeReS is a cli tool to manageNewRelic Syntheticsmonitors with a Synthetics Lite account
(Pro should work too). The tool emulates the actions of a user in the browser
and doesn’t use the Synthetics API since that’s only available to the Pro
accounts.With this tool you can:List all your monitors, including their success rate, locations,
notifications etc.Create, update and delete monitorsList available locations for monitor installation andeverything else available though the Web console.Installation$pipinstallneresConfigurationYou will need a newrelic accountStart by using thelogincommandRead the docs or run–helpUseLogin to NewRelicLogin to NewRelic with thelogincommand:$neresloginIf you have multiple NewRelic accounts you can have different environments:$neres--environmentworkloginNoteDefault environment is namednewrelic. Remember to always pass–environmentto all neres commands to execute them in the correct environment. Alternatively
you can addNERES_ENVIRONMENTto your environment variables list.List AccountsYou can list all the accounts connected to the email you used to connect using:$nereslist-accountsBy default neres will act on the first account listed. This command will help
you select a different account by using theIDof the account in combination
with the–accountoption or by settingNERES_ACCOUNTin your environment.List LocationsLists available monitor locations:$nereslist-locationsList MonitorsLists available monitors:$nereslist-monitorsYou can only list IDs of the monitors:$nereslist-monitors--ids-onlyOr get the raw JSON output from NewRelic:$nereslist-monitors--rawAdd MonitorAdds a Synthetics monitor:$neresadd-monitormonitorNamehttp://example.comUse–helpto get a full list of supported options for the command. All
options are optional.Get MonitorGet details on a monitor$neresget-monitorde310b69-3195-435e-b1ef-3a0af67499deNoteYou can uselist-monitorsto get a list of available monitors.Update MonitorUpdate an existing monitor$neresupdate-monitorde310b69-3195-435e-b1ef-3a0af67499de--name"Foobar"Use–helpto get a full list of supported options for the command. All
options are optional.Open MonitorOpen monitor in the browser$neresopende310b69-3195-435e-b1ef-3a0af67499deGet state of all monitors of accountExport the state of all monitors of account in YAML format with$neresget-stateSave the output into a file, edit and use in combination with theupdate-from-statefilecommand to update multiple monitors at once.$neresget-state>state.yamlUpdate monitors from statefileUpdate monitors from statefile with$neresupdate-from-statefilestate.yamlNoteThis command will only update existing monitors. It will not create new or
delete existing ones.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.4.0 (2017-06-02)Use NewRelic’s “stoplight” to determine monitor health.0.3.0 (2017-05-25)Multiple monitors management with get-state and update-from-statefileBug fixes0.2.0 (2017-05-11)Python 3 fixesBugfixesTests!0.1.1 (2017-04-27)First release on PyPI.0.1.0 (2017-04-12)Cookiecutter import
Package: nereval
Evaluation script for named entity recognition (NER) systems based on entity-level F1 score.DefinitionThe metric as implemented here has been described by Nadeau and Sekine (2007) and was widely used as part of the Message Understanding Conferences (Grishman and Sundheim, 1996). It evaluates an NER system according to two axes: whether it is able to assign the right type to an entity, and whether it finds the exact entity boundaries. For both axes, the number of correct predictions (COR), the number of actual predictions (ACT) and the number of possible predictions (POS) are computed. From these statistics, precision and recall can be derived:precision = COR/ACT
recall = COR/POSThe final score is the micro-averaged F1 measure of precision and recall of both type and boundary axes.InstallationpipinstallnerevalUsageThe script can either be used from within Python or from the command line when classification results have been written to a JSON file.Usage from Command LineAssume we have the following classification results ininput.json:[{"text":"CILINDRISCHE PLUG","true":[{"text":"CILINDRISCHE PLUG","type":"Productname","start":0}],"predicted":[{"text":"CILINDRISCHE","type":"Productname","start":0},{"text":"PLUG","type":"Productname","start":13}]}]Then the script can be executed as follows:pythonnereval.pyinput.jsonF1-score:0.33Usage from PythonAlternatively, the evaluation metric can be directly invoked from within python. Example:importnerevalfromnerevalimportEntity# Ground-truth:# CILINDRISCHE PLUG# B_PROD       I_PRODy_true=[Entity('CILINDRISCHE PLUG','Productname',0)]# Prediction:# CILINDRISCHE PLUG# B_PROD       B_PRODy_pred=[# correct type, wrong textEntity('CILINDRISCHE','Productname',0),# correct type, wrong textEntity('PLUG','Productname',13)]score=nereval.evaluate([y_true],[y_pred])print('F1-score:%.2f'%score)F1-score:0.33Note on SymmetryThe metric itself is not symmetric due to the inherent problem of word overlaps in NER. Soevaluate(y_true, y_pred) != evaluate(y_pred, y_true). This comes apparent if we consider the following example (tagger uses an BIO scheme):# Example 1:Input:CILINDRISCHEPLUGDIN908M10X1FooTruth:B_PRODI_PRODB_PRODB_DIMOPredicted:B_PRODB_PRODB_PRODB_PRODB_PRODCorrectText:2CorrectType:2# Example 2 (inversed):Input:CILINDRISCHEPLUGDIN908M10X1FooTruth:B_PRODB_PRODB_PRODB_PRODB_PRODPredicted:B_PRODI_PRODB_PRODB_DIMOCorrectText:2CorrectType:3Notes and ReferencesUsed in a student research project on natural language processing atUniversity of Twente, Netherlands.ReferencesGrishman, R., & Sundheim, B. (1996).Message understanding conference-6: A brief history.In COLING 1996 Volume 1: The 16th International Conference on Computational Linguistics(Vol. 1).Nadeau, D., & Sekine, S. (2007).A survey of named entity recognition and classification.Lingvisticae Investigationes, 30(1), 3-26.
Package: ner-eval-dashboard
ner-eval-dashboardDashboard for Quality-driven NER.conceptThe idea of this project is to provide a more elaborated evaluation for NER models.
That way, it should be easier to fix labeling mistakes,
better understand the positive and negative aspects of the trained NER model and see how it applies on unlabeled data.progressversion0.1.0provides standard F1 scores for Exact Match, Type Match, and Position Match. So far onlyFlairmodels are implemented.
SeeIssuesto view planned featuresinstallationThe ner eval dashboard can be installed via:pip install ner-eval-dashboard==0.1usageThe ner eval dashboard can be used on various ways: cli, api or via docker.
It is important to always specify a model, a dataset and a tokenizer.Note:To run the examples, you need to once manually download the CONLL03 dataset and put it into the{FLAIR_CACHE_ROOT}/.flair/datasets/conll_03folder.cliThe ner eval dashboard can be use via the command line interface:ner_eval_dashboard [--dataset_path DATASET_PATH] [--extra_unlabeled_data EXTRA_UNLABELED_DATA] [--use USE [USE ...]] [--exclude EXCLUDE [EXCLUDE ...]] {FLAIR} predictor_name_or_path {SPACE} {RAW,COLUMN_DATASET,JSONL_DATASET,CONLL03,CONLL03_GERMAN,CONLL03_DUTCH,CONLL03_SPANISH,WNUT17,ARABIC_ANER,ARABIC_AQMAR,BASQUE,WEIBO,DANE,MOVIE_SIMPLE,MOVIE_COMPLEX,SEC_FILLINGS,RESTAURANT,STACKOVERFLOW,TWITTER
,PERSON,WEBPAGES,WNUT2020,WIKIGOLD,FINER,BIOFID,EUROPARL,LEGAL_NER,GERMEVAL,POLITICS,BUSINESS,ICELANDIC_NER,HIRONSAN,MASAKHANE,MULTI_CONER,WIKIANN,XTREME,WIKINER,SWEDISH_NER,TURKU}For example the following can be used to evaluate the Bi-LSTM-CRF model based on Flair embeddings on CONLL03:ner_eval_dashboard FLAIR flair/ner-english SPACE CONLL03apifromner_eval_dashboard.dataset.flairimportFlairConll03fromner_eval_dashboard.predictorimportFlairPredictorfromner_eval_dashboard.tokenizerimportSpaceTokenizerfromner_eval_dashboard.appimportcreate_apptokenizer=SpaceTokenizer()dataset=FlairConll03(tokenizer)predictor=FlairPredictor("flair/ner-english")app=create_app("my-app",predictor,dataset)app.run_server()dockerdocker images are publicly available atdocker hubdocker run -it --rm -p 8050:8050 helpmefindaname/ner-eval-dashboard FLAIR flair/ner-english SPACE CONLL03