Saravanan666/pypi-org huggingface dataset:
Package: core-module-auto-keyref-app
core_module_auto_keyref_appAuto Keyref module for the parser core project.Quick start1. Add “core_module_auto_keyref_app” to your INSTALLED_APPS settingINSTALLED_APPS=[...'core_module_auto_keyref_app',]PARSER_AUTO_KEY_KEYREF=True2. Include the core_module_auto_keyref_app URLconf in your project urls.pyurl(r'^',include('core_module_auto_keyref_app.urls')),
Package: core-module-blob-host-app
core_module_blob_host_appBlob Host module for the parser core project.Quick start1. Add “core_module_blob_host_app” to your INSTALLED_APPS settingINSTALLED_APPS=[...'core_module_blob_host_app',]2. Include the core_module_blob_host_app URLconf in your project urls.pyurl(r'^',include('core_module_blob_host_app.urls')),
Package: core-module-chemical-composition-app
core_module_chemical_composition_appChemical composition module for the parser core project.Quick start1. Add “core_module_chemical_composition_app” to your INSTALLED_APPS settingINSTALLED_APPS=[...'core_module_chemical_composition_app',]2. Include the core_module_chemical_composition_app URLconf in your project urls.pyurl(r'^',include('core_module_chemical_composition_app.urls')),
Package: core-module-chemical-composition-simple-app
core_module_chemical_composition_simple_appChemical composition simple module for the parser core project.Quick start1. Add “core_module_chemical_composition_simple_app” to your INSTALLED_APPS settingINSTALLED_APPS=[...'core_module_chemical_composition_simple_app',]2. Include the core_module_chemical_composition_simple_app URLconf in your project urls.pyurl(r'^',include('core_module_chemical_composition_simple_app.urls')),
Package: core-module-excel-uploader-app
core_module_excel_uploader_appExcel Uploader module for the parser core project. A sample file is provided in the static folder.Quick start1. Add “core_module_excel_uploader_app” to your INSTALLED_APPS settingINSTALLED_APPS=[...'core_module_excel_uploader_app',]2. Include the core_module_excel_uploader_app URLconf in your project urls.pyurl(r'^',include('core_module_excel_uploader_app.urls')),
Package: core-module-fancy-tree-registry-app
core_module_fancy_tree_registry_appFancy Tree module for the parser core project.Quick start1. Add “core_module_fancy_tree_registry_app” to your INSTALLED_APPS settingINSTALLED_APPS=[...'core_module_fancy_tree_registry_app',]2. Include the core_module_fancy_tree_registry_app URLconf in your project urls.pyurl(r'^',include('core_module_fancy_tree_registry_app.urls')),
Package: core-module-local-id-registry-app
core_module_local_id_registry_appLocal ID registry moduleQuick start1. Add “core_module_local_id_registry_app” to your INSTALLED_APPS settingINSTALLED_APPS=[...'core_module_local_id_registry_app',]2. Include the core_module_local_id_registry_app URLconf in your project urls.pyurl(r'^',include('core_module_local_id_registry_app.urls')),
Package: core-module-periodic-table-app
core_module_periodic_table_appPeriodic Table module for the parser core project.Quick start1. Add “core_module_periodic_table_app” to your INSTALLED_APPS settingINSTALLED_APPS=[...'core_module_periodic_table_app',]2. Include the core_module_periodic_table_app URLconf in your project urls.pyurl(r'^',include('core_module_periodic_table_app.urls')),
Package: core-module-raw-xml-app
core_module_raw_xml_appRaw XML module for the parser core project.Quick start1. Add “core_module_raw_xml_app” to your INSTALLED_APPS settingINSTALLED_APPS=[...'core_module_raw_xml_app',]2. Include the core_module_raw_xml_app URLconf in your project urls.pyurl(r'^',include('core_module_raw_xml_app.urls')),
Package: core-module-remote-blob-host-app
core_module_remote_blob_host_appRemote Blob Host module for the parser core project.Quick start1. Add “core_module_remote_blob_host_app” to your INSTALLED_APPS settingINSTALLED_APPS=[...'core_module_remote_blob_host_app',]2. Include the core_module_remote_blob_host_app URLconf in your project urls.pyurl(r'^',include('core_module_remote_blob_host_app.urls')),
Package: core-module-status-registry-app
core_module_status_registry_appStatus registry moduleQuick start1. Add “core_module_status_registry_app” to your INSTALLED_APPS settingINSTALLED_APPS=[...'core_module_status_registry_app',]2. Include the core_module_status_registry_app URLconf in your project urls.pyurl(r'^',include('core_module_status_registry_app.urls')),
Package: core-module-text-area-app
core_module_text_area_appText Area module for the parser core project.Quick start1. Add “core_module_text_area_app” to your INSTALLED_APPS settingINSTALLED_APPS=[...'core_module_text_area_app',]2. Include the core_module_text_area_app URLconf in your project urls.pyurl(r'^',include('core_module_text_area_app.urls')),
Package: corenetworks
corenetworksDiscontinued:This project is no longer maintained.Python library for thehttps://core-networks.deDNS API. You can find the full documentation athttps://corenetworks.geekdocs.de.ContributorsSpecial thanks goes to allcontributors. If you would like to contribute,
please see theinstructions.LicenseThis project is licensed under the MIT License - see theLICENSEfile for details.
Package: core-news
CORE NEWS & EVENTSIt is a module of the Research CORE ERM which implements the models related to news & events.Mailing listsMessagesSubjectsCommunications templatesCommunicationsInstallationpipinstallcore-newsEnterprise Resources Management- Research CORECORE is an ERM platform developed in within a research environment and aims to automatize
internal management processes, like human resources, orders, budgets, suppliers, funds, equipment \rooms accesses and maintenance and publications.The project is distributed as an open-source project to facilitate the adaptation of the
ERM to each institution's needs.
Package: corenlp-client
corenlp-clientprerequisitesJava 8Python >= 3.6DescriptionA simple, user-friendly python wrapper for Stanford CoreNLP, an nlp tool for natural language processing in Java.
CoreNLP provides a lingustic annotaion pipeline, which means users can use it to tokenize, ssplit(sentence split), POS, NER, constituency parse, dependency parse, openie etc. However, it's written in Java, which can not be interacted directly with Python programs. Therefore, we've developed a CoreNLP client tool in Python.
The corenlp-client can be used to start a CoreNLP Server once you've followed the officialreleaseand download necessary packages and corresponding models. Or, if a server is already started, the only thing you need to do is to specify the server's url, and call the annoate method.Installationpip install corenlp_clientUsageQuick start:Sometimes you may want to get directly tokenized (pos, ner) result in list format without extra efforts. So, we providetokenize(), pos_tag(), ner()methods to simplify the whole process.fromcorenlp_clientimportCoreNLP# max_mem: max memory use, default is 4. threads: num of threads to use, defualt is num of cpu cores.annotator=CoreNLP(annotators="tokenize",corenlp_dir="/path/to/corenlp",local_port=9000,max_mem=4,threads=2)# by setting `ssplit=False`, you'll get a list of tokens without splitting sentencestokenized_text=annotator.tokenize(data,ssplit=False)pos_tags=annotator.pos_tag(data)ners=annotator.ner(data)annotator.close()Start a new server and annotate text:If you want to start a server locally, it's more graceful to usewith ... as ...to handle exceptions.fromcorenlp_clientimportCoreNLP# max_mem: max memory use, default is 4. threads: num of threads to use, defualt is num of cpu cores.withCoreNLP(annotators="tokenize",corenlp_dir="/path/to/corenlp",local_port=9000,max_mem=4,threads=2)asannotator:# your code hereUse an existing server:You can also use an existing server by providing the url.fromcorenlp_clientimportCoreNLP# lang for language, default is en.# you can specify annotators to use by passing `annotator="tokenize,ssplit"` args to CoreNLP. If not provided, all available annotators will be used.withCoreNLP(url="https://corenlp.run",lang="en")asannotator:# your code hereAdvanced UsageFor advanced users, you may want to have access to server's original response in dict format:anno=annotator.annotate("CoreNLP is your one stop shop for natural language processing in Java! Enjoy yourself! ")print(anno.tokens)# tokensprint(anno.parse_tree)# parseprint(anno.bi_parse_tree)# binaryParseprint(anno.basic_dep)# basicDependenciesprint(anno.enhanced_dep)# enhancedDependenciesprint(anno.enhanced_pp_dep)# enhancedPlusPlusDependenciesprint(anno.entities)# entitymentionsprint(anno.openie)# openieprint(anno.ann_result)# original server's response formatprint(annotator.pretty_print_tree(anno.parse_tree[0]))# pretty print parse tree's structureExtra NotesIf you choose to start server locally, it'll take a while to load models for the first time you annotate a sentence.For timeout error, a simple retry may be useful.Also, if "with" is not used, remember to call close() method to stop the Java CoreNLP server.
Package: corenlp-protobuf
This package contains python bindings forStanford
CoreNLP’s protobuf
specifications, as generated byprotoc. These bindings can used to
parse binary data produced by, e.g., theStanford CoreNLP
server.Usage:fromcorenlp_protobufimportDocument,parseFromDelimitedString# document.dat contains a serialized Document.withopen('document.dat','r')asf:buf=f.read()doc=Document()parseFromDelimitedString(doc,buf)# You can access the sentences from doc.sentence.sentence=doc.sentence[0]# You can access any property within a sentence.print(sentence.text)# Likewise for tokenstoken=sentence.token[0]print(token.lemma)Seetest_read.pyfor more examples.
Package: corenlp-vdep
Convert Stanford CoreNLP's Dependency Tree to Spacy's for VisualizationThis module provides a format converter from Stanford CoreNLP's dependency trees to Spacy's such that the visualation can be done using Spacy's visualizer.MotivationI just found that Spacy has an amazing visualizer that we should explore more and this project bridges the gap between the CoreNLP parsing outputs and it.Pre-requisiteInstall Spacy and stanfordnlp. The versions we have tested are Spacy 2.2.2 and stanfordnlp 0.2.0pip install spacy stanfordnlpDownload Stanford CoreNLP (tested on 3.9.2) Java library:https://stanfordnlp.github.io/CoreNLP/Set the env variable for stanfordnlp:export CORENLP_HOME=/yourhome/stanford-corenlp-full-2018-10-05Installationpip install corenlp-vdepExamplesimportspacyfromspacyimportdisplacyfromstanfordnlp.serverimportCoreNLPClientfromcorenlp_dtree_visualizer.convertersimport_corenlp_dep_tree_to_spacy_dep_tree# Input texttext='Jim killed John with a joke.'# Get a dependency tree from a Stanford CoreNLP pipelinewithCoreNLPClient(annotators=['tokenize','ssplit','pos','lemma','ner','parse','depparse'],timeout=60000,memory='8G',output_format='json')asclient:# submit the request to the serverann=client.annotate(text)# Convert dependency tree formatssent=ann['sentences'][0]tree=_corenlp_dep_tree_to_spacy_dep_tree(sent['tokens'],sent['enhancedPlusPlusDependencies'])# Visualize with Spacynlp=spacy.load("en_core_web_sm")displacy.render(tree,style="dep",manual=True)# could also save to a file# svg = displacy.render(tree, style="dep", manual=True)# with open('tmp.svg', 'w', encoding='utf-8') as fw:# fw.write(svg)
Package: corenlp-xml-reader
# corenlp-xml-reader
A utility for easily working with CoreNLP-annotated text in Python.## About this package
Other than reading the xml file into a python representation, this utility
does some necessary “fixing up” of the data in CoreNLP.
[to be described]## Quickstart### Install
Install from the Python Package Index:`bash pip installcorenlp-xml-reader`Alternatively, install a version you can hack on:`bash git clonehttps://github.com/enewe101/corenlp-xml-reader.gitcdcorenlp-xml-readerpython setup.py develop `### Use[Add basic usage examples.]
Package: corenlpy
# corenlpy
A utility for easily working with CoreNLP-annotated text in Python.## About this package
Other than reading the xml file into a python representation, this utility
does some necessary “fixing up” of the data in CoreNLP.
[to be described]## Quickstart### Install
Install from the Python Package Index:`bash pip install corenlpy `Alternatively, install a version you can hack on:`bash git clonehttps://github.com/enewe101/corenlpy.gitcd corenlpy python setup.py develop `
Package: core-oaipmh-common-app
Base OAI-PMH sharing functions for the curator core project.Quickstart1. Add “core_oaipmh_common_app” to your INSTALLED_APPS settingINSTALLED_APPS=[...'core_oaipmh_common_app',]2. Include the core_oaipmh_common_app URLconf in your project urls.pyurl(r'^oaipmh/',include('core_oaipmh_common_app.urls')),
Package: core-oaipmh-harvester-app
OAI-PMH harvesting capabilities for the curator core project.Quickstart1. Add “core_oaipmh_harvester_app” to your INSTALLED_APPS settingINSTALLED_APPS=[...'core_oaipmh_harvester_app',]2. Include the core_oaipmh_harvester_app URLconf in your project urls.pyurl(r'^oai_pmh/',include('core_oaipmh_harvester_app.urls')),
Package: core-oaipmh-provider-app
OAI-PMH provider capabilities for the curator core project.Quickstart1. Add “core_oaipmh_provider_app” to your INSTALLED_APPS settingINSTALLED_APPS=[...'core_oaipmh_provider_app',]2. Include the core_oaipmh_provider_app URLconf in your project urls.pyurl(r'^oai_pmh/',include('core_oaipmh_provider_app.urls')),
Package: core-optimizer
IntroductionThe module core_optimizer provides a PyTorch implementation of the Continual Resilient (CoRe) optimizer.
The CoRe optimizer is a first-order gradient-based optimizer for stochastic and deterministic iterative optimizations.
It applies weight-specific learning rate adaption depending on the optimization progress.
Its algorithm combines Adam- and RPROP-like step size updates and employs weight regularization and decay.InstallationThe module core_optimizer can be installed using pip3 once the repository has been cloned.gitclone<core_optimizer-repository>pip3install./core_optimizerA non super user can install the package using a virtual environment or the--userflag.
If there is no space left on device for TMPDIR, one can useTMPDIR=<PATH>in front of pip3,
with <PATH> being a directory with more space for temporary files.UsageThe CoRe optimizer can be applied in the same way as the optimizers in torch.optim.
The only exception is the import of the optimizer.fromcore_optimizerimportCoReoptimizer=CoRe(...)# dots are placeholders for model parameters and optimizer hyperparametersFor comparison the following code block shows the usage of the Adam optimizer from torch.optim.fromtorch.optimimportAdamoptimizer=Adam(...)# dots are placeholders for model parameters and optimizer hyperparametersThe minimal workflow of training a PyTorch model is shown in the next code block.
Examples which are ready to go can be found in test_core_optimizer.py.importtorchfromcore_optimizerimportCoRe# set up input and expected outputs for traininginputs=torch.tensor(...)# dots are placeholders for input datalabels=torch.tensor(...)# dots are placeholders for expected output data# define loss function, model, and optimizerloss_fn=torch.nn.modules.loss.TorchLoss()# TorchLoss is a placeholder for any Torch loss functionmodel=TorchModel()# TorchModel is a placeholder for any Torch modeloptimizer=CoRe(model.parameters(),step_sizes=(1e-6,1e-2))# define CoRe optimizer# run training stepsforiinrange(...):# dots are a placeholder for number of stepsoptimizer.zero_grad()# zero optimizer's gradientsoutputs=model(inputs)# predict outputloss=loss_fn(outputs,labels)# calculate lossloss.backward()# calculate loss gradientoptimizer.step()# adjust model parametersThe algorithm and all hyperparameters of the CoRe optimizer are explained in the filedocs.pdf.License and Copyright InformationThe module core_optimizer is distributed under the BSD 3-Clause “New” or “Revised” License.
For more license and copyright information, see the fileLICENSE.txt.How to CiteWhen publishing results obtained with the CoRe optimizer, please cite
M. Eckhoff, M. Reiher,Lifelong Machine Learning Potentials, J. Chem. Theory Comput. 19, 3509-3525 (2023)
and
M. Eckhoff, M. Reiher,CoRe Optimizer: An All-in-One Solution for Machine Learning, arXiv:2307.15663 [cs.LG] (2023).Support and ContactIn case you encounter any problems or bugs, please write a message tolifelong_ml@phys.chem.ethz.ch.
Package: core-orders
CORE ORDERSIt is a module of the Research CORE ERM which implements the models related to orders.OrdersExpense codesInstallationpipinstallcore-ordersEnterprise Resources Management- Research CORECORE is an ERM platform developed in within a research environment and aims to automatize
internal management processes, like human resources, orders, budgets, suppliers, funds, equipment \rooms accesses and maintenance and publications.The project is distributed as an open-source project to facilitate the adaptation of the
ERM to each institution's needs.
Package: core-parser-app
XSD parser, renderers and modules for the curator core project.Quick start1. Add “core_parser_app” to your INSTALLED_APPS settingINSTALLED_APPS=[..."core_parser_app","core_parser_app.tools.modules","core_parser_app.tools.parser",]2. Include the core_parser_app URLconf in your project urls.pyurl(r'^parser/',include("core_parser_app.urls")),Migration to the Bootstrap modalBreaking changes during the migration from the jQuery UI modal to the Bootstrap modal for the module :The JavaScript global variablesopenModule,openPopUpandinitialStatefrom the popup.js file have been deleted.The JavaScript variableopenModulehas been renamed tomoduleElement.The modal management has been changed. JQuery UI reused the same DOM element for all modal loaded in the page. Bootstrap use a unique DOM element for each modal. Consequently the main modal container will have the following unique ID :modal-{{module_id}}.The save callback uses the new variableopenModuleto get the unique DOM container and get the value to save.The Django form id must be changed be because it is not unique and can create conflict if the page includes a module more than one time (example with the BLOBHostForm):form=BLOBHostForm()form.fields["file"].widget.attrs.update({"id":"file-input-%s"%str(module_id)})
Package: core-people
CORE PEOPLEIt is a module of the Research CORE ERM which implements the models related to people & groups.People infoPeople academic degreePeople degreesAffiliationGroupsGroups membership & positions.Installationpipinstallcore-ordersEnterprise Resources Management- Research CORECORE is an ERM platform developed in within a research environment and aims to automatize
internal management processes, like human resources, orders, budgets, suppliers, funds, equipment \rooms accesses and maintenance and publications.The project is distributed as an open-source project to facilitate the adaptation of the
ERM to each institution's needs.
Package: core-permissions
CORE PERMISSIONSIt is a module of the Research CORE ERM which implements the models related to permissions.Installationpipinstallcore-permissionsEnterprise Resources Management- Research CORECORE is an ERM platform developed in within a research environment and aims to automatize
internal management processes, like human resources, orders, budgets, suppliers, funds, equipment \rooms accesses and maintenance and publications.The project is distributed as an open-source project to facilitate the adaptation of the
ERM to each institution's needs.
Package: coreplus
🚀 Django Core PlusDescriptionDjango functional modules.InstallationWithin a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.UsageUse examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.SupportTell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.RoadmapIf you have ideas for releases in the future, it is a good idea to list them in the README.Test and BuildTodoContributingState if you are open to contributions and what your requirements are for accepting them.For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.Authors and acknowledgmentShow your appreciation to those who have contributed to the project.LicenseFor open source projects, say how it is licensed.Project statusIf you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
Package: corepluslib
🚀 Django Core PlusDescriptionDjango functional modules.InstallationWithin a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.UsageUse examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.SupportTell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.RoadmapIf you have ideas for releases in the future, it is a good idea to list them in the README.Test and BuildTodoContributingState if you are open to contributions and what your requirements are for accepting them.For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.Authors and acknowledgmentShow your appreciation to those who have contributed to the project.LicenseFor open source projects, say how it is licensed.Project statusIf you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.