Saravanan666/pypi-org huggingface dataset:
Package: model-personal-trainer
Personal TrainerPersonal Trainer is a Python library for easily training models on your own data using open source models.Personal Trainer is still under development.UsageEach type of model is represented by a class in Personal Trainer. For example, one of the simplest models to use is a TextClassifier.from personal_trainer import TextClassifier

classifier = TextClassifier()A text classifier takes some text and returns whether it's thing A or thing B. To train the model
you first construct a series of examples of text and how it should be classified. You can then pass
those examples to thetrainmethod of the classifier.examples = [
        ('oscar meyer', 'hot dog'),
        ('choripan', 'hot dog'),
        ('bratwurst', 'hot dog'),
        ('vienna sausage', 'hot dog'),
        ('sandwich', 'not'),
        ('burger', 'not'),
        ('dumpling', 'not'),
        ('bun', 'not'),
]

classifier.train(examples)Once you've trained your model you can then use it. For a classifier that means calling theclassifymethod.res = classifier.classify('taco')
print('res:', res)If training was successfulresshould benot. A classifier output can be any string, integer, or Boolean.To save your model for later use using thesavemethod. This writes the model out to the path that you
provide.classifier.save('hot-dog-or-not.model')To load the model later, provide the same path when constructing the classifier.classifier = TextClassifier('hot-dog-or-not.model')
Package: model-pool
align-model-poolVarious alignment models for colab and hf-spacesInstall itpipinstallmodel-pool# or poetry add model-pool# pip install git+htts://github.com/ffreemt/align-model-pool# poetry add git+htts://github.com/ffreemt/align-model-pool# To upgradepipinstallmodel-pool-U# or poetry add model-pool@latestUse itimportmodel_pool
Package: model-porter
model-porterInternal tool for seeding content in Django (and Wagtail). Please bear with us while we prepare more detailed documentation.Compatibilitymodel-porter' major.minor version number indicates the Django release it is compatible with. Currently this is Django 4.1.InstallationInstall usingpip:pipinstallmodel-porterAddmodel_porterto yourINSTALLED_APPSsetting:INSTALLED_APPS=[# ...'model_porter'# ...]
Package: modelr
========================Welcome to modelr server========================.. image:: https://img.shields.io/badge/license-Apache-blue.svg:target: https://github.com/agile-geoscience/modelr/blob/develop/LICENSE.md:alt: Apache 2 license.. image:: http://img.shields.io/pypi/dw/modelr.svg:target: http://pypi.python.org/pypi/modelr/:alt: PyPI downloads.. image:: https://img.shields.io/pypi/v/modelr.svg:target: http://pypi.python.org/pypi/modelr/:alt: PyPI version.. image:: http://img.shields.io/github/issues/badges/modelr.svg:target: https://github.com/agile-geoscience/modelr:alt: GitHub issuesThis is the documentation for the back-end modelr server software. You might be looking for `modelr.io <https://www.modelr.io/>`_Modelr server is a Google-Charts-like API for geophysics! Submit tasks via a URL.Example:* `<https://www.modelr.org/plot.jpeg?script=wedge_spatial.py&theta=0&f=25&colourmap=Greys&base1=variable-density&overlay1=none&base2=earth-model&overlay2=wiggle&opacity=0.5&Rock0=3000.0%2C1600.0%2C2500.0%2C%0A%09%20%2050.0%2C%2050.0%2C%2050.0&Rock1=2770.0%2C1780.0%2C2185.0%2C%0A%09%20%2050.0%2C%2050.0%2C%2050.0&Rock2=3000.0%2C1600.0%2C2500.0%2C%0A%09%20%2050.0%2C%2050.0%2C%2050.0&tslice=0.15&scale=1.0%2C99&type=scenario>`_Full interface+++++++++++++* There's a full interface for modelr at `modelr.io <https://www.modelr.io/>`_Prerequisites++++++++++++++++You will need scientific python (numpy, scipy, matplotlib), which come withEnthought Canopy:  `<http://www.enthought.com>`_They can alternatively be installed via pip, aptitude, ports, or sourced from git.Additionally, ImageMagick will need to be installed in order to handle conversion of svg to png. Binaries can be downloaded from the ImageMagick website:`<http://www.imagemagick.org/script/binary-releases.php#unix>`_ImageMagick can also be installed via ports or aptitude.Other python packages that will be automatically installed during setup:* agilegeo* pypng* requests* jinja2* svgwriteLinks+++++++++++* `Agile Geoscience <http://www.agilegeoscience.com>`_* `Homepage <http://agile-geoscience.github.com/modelr/>`_* `Issue Tracker <https://github.com/agile-geoscience/modelr/issues/>`_* `PyPi <http://pypi.python.org/pypi/modelr/>`_* `Github <https://github.com/agile-geoscience/modelr>`_Authors++++++++++++++++* `Matt Hall <https://github.com/kwinkunks>`_ @ `Agile Geoscience <http://www.agilegeoscience.com>`_* `Sean Ross-Ross <https://github.com/srossross>`_ @ now at `Continuum`* `Evan Bianco <https://github.com/EvanBianco>`_ @ `Agile Geoscience <http://www.agilegeoscience.com>`_* `Ben Bougher <https://github.com/ben-bougher>`_
Package: model-railway-signals
model-railway-signallingA DCC model railway signalling application written in Python, enabling automated and interlocked layout
signalling schemes to be designed and configured via the UI without the need to write any code. The
application is primarily intended for the Raspberry Pi, but will also run on other platforms (albeit
without some of the Raspberry-Pi specific interfacing functions).Enables layout schematics to be created with signals, points, track sections and block instrumentsSupports most types of UK colour light signals, semaphore signals, and ground signals.Interfaces with the Pi-SPROG DCC command station to drive the signals and points out on the layoutUses the Raspberry Pi GPIO inputs to provide train detection in support of signalling automationIncorporates MQTT networking to allow multiple signalling applications to be linked for larger layoutsConfigured / pre-installed systems are now available to purchase from:https://www.model-railway-signalling.co.uk/What's new for Release 4.0:Hidden files and folders now supressed in file load and save dialogsNetwork heartbeats and monitering to check the status of other network nodesAbility to trigger application shutdown of connected network nodes on application exitSupport for the Pi-SPROG3 V2 DCC Programmer / Controller (higher baud rate)Quickstart guide updated to reflect settings changes for the Pi-SPROG3 V2BREAKING CHANGE - GPIO Ports 16 and 17 no longer selectable for track sensorsMinor bugfixes and application enhancements to improve the overall user experienceBug reports and feedback are welcome and appreciated:What aspects are intuitive? What aspects aren't?What aspects do you particularly like?What aspects particularly irritate you?What new features would you like to see?Installing the applicationFor a first time installation use:$ python3 -m pip install model-railway-signalsTo upgrade to the latest version use:$ python3 -m pip install --upgrade model-railway-signalsIf you want to use Block Instruments with full sound enabled (bell rings and telegraph key sounds)
then you will also need to install the 'simpleaudio' package. Note that for Windows it has a dependency
on Microsoft Visual C++ 14.0 or greater (so you will need to ensure Visual Studio 2015 is installed first).
If 'simpleaudio' is not installed then the software will still function correctly (just without sound).$ python3 -m pip install simpleaudioRunning the applicationThe python package should be run as a module (note underscores):$ python3 -m model_railway_signalsIf required, a layout schematic can be loaded at startup:$ python3 -m model_railway_signals -f layout_file.sigIf required, the logging level can be specified at startup (ERROR, WARNING, INFO or DEBUG)$ python3 -m model_railway_signals -f layout_file.sig -l DEBUGDocumentation, in the form of a Quick-Start guide can be found in the 'user_guide' folder:https://github.com/johnrm174/model-railway-signalling/tree/main/user_guideSome example layout configuration files can be found in the 'configuration_examples' folder:https://github.com/johnrm174/model-railway-signalling/tree/main/configuration_examples
Package: model-reduction
model_reductionA Python package to aggregate and reduce water distribution network modelsFree software: MIT licenseDocumentation:https://model-reduction.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2021-04-13)First release on PyPI.
Package: model-reference
``django-model-reference`` defines a simple :cls:`model_reference.Reference`model that hold named references to specific instances of any Django model.Usage=====References are controlled by the :func:`register()` and :func:`load` functions ofthe ``model_reference`` module. These function are also mirrored by thecorresponding methods in the ``Reference.objects`` manager. To register areference, simply pass the object and either a unique name or a unique suffix::from model_reference import register, loadfrom django.contrib.auth.models import User# Fetch some objectsjohn = User.objects.get(username='john')ringo = User.objects.get(username='ringo')# Register references in the databaseregister(john, 'favorite beatle')register(ringo, suffix='drummer')The name for each reference is unique. If the register function is called withthe explicit ``suffix`` argument, it will register the instance as``<app_label>.<model>:suffix``, which in our case becomes ``'auth.user:drummer'``.In order to retrieve the objects referred by the Reference object, we can usethe :func:`load` function::>>> load('favorite beatle')<User: john>>>> load('drummer', model=User)<User: ringo>Objects registered with the suffix form of the register function should pass amodel class in order to construct the complete name string. This is equivalentas creating the string by hand:>>> load('auth.user:drummer')<User: ringo>Empty references================If one request a non-existing reference to the load() function, it raises an:error:`model_reference.EmptyReferenceError`. Users can register factoryfunctions to create these objects on-the-fly.The default way of registering factory function is via the :func:`model_reference.factory`decorator. It must be called with a reference string (or suffix + model) anddecorates a function that return a new instance and is called without arguments::@factory('drummer', model=User)def make_ringo():user, created = User.object.get_or_create(username=ringo,first_name='Richard'last_name='Starkley',email='ringo@applerecords.co.uk')return userIf a new instance is created, the function is responsible to save it in thedatabase
Package: model-registry
Model Registry Python ClientThis library provides a high level interface for interacting with a model registry server.Basic usagefrommodel_registryimportModelRegistryregistry=ModelRegistry(server_address="server-address",port=9090,author="author")model=registry.register_model("my-model",# model name"s3://path/to/model",# model URIversion="2.0.0",description="lorem ipsum",model_format_name="onnx",model_format_version="1",storage_key="aws-connection-path",storage_path="path/to/model",metadata={# can be one of the following types"int_key":1,"bool_key":False,"float_key":3.14,"str_key":"str_value",})model=registry.get_registered_model("my-model")version=registry.get_model_version("my-model","v2.0")experiment=registry.get_model_artifact("my-model","v2.0")Default values for metadataIf not supplied,metadatavalues defaults to a predefined set of conventional values.
Reference the technical documentation in the pydoc of the client.Importing from Hugging Face HubTo import models from Hugging Face Hub, start by installing thehuggingface-hubpackage, either directly or as an
extra (available asmodel-registry[hf]).
Models can be imported withhf_model=registry.register_hf_model("hf-namespace/hf-model",# HF repo"relative/path/to/model/file.onnx",version="1.2.3",model_name="my-model",description="lorem ipsum",model_format_name="onnx",model_format_version="1",)There are caveats to be noted when using this method:It's only possible to import a single model file per Hugging Face Hub repo right now.If the model you want to import is in a global namespace, you should provide an author, e.g.hf_model=registry.register_hf_model("gpt2",# this model implicitly has no author"onnx/decoder_model.onnx",author="OpenAI",# Defaults to unknown in the absence of an authorversion="1.0.0",description="gpt-2 model",model_format_name="onnx",model_format_version="1",)DevelopmentCommon tasks, such as building documentation and running tests, can be executed usingnoxsessions.Usenox -lto list sessions and execute them usingnox -s [session].Running Locally on Mac M1 or M2 (arm64 architecture)If you want run tests locally you will need to set up a colima develeopment environment using the instructionshereYou will also have to change the package source to one compatible with ARM64 architecture. This can be actioned by uncommenting lines 14 or 15 in the pyproject.toml file. Run the following command after you have uncommented the line.poetrylockUse the following commands to directly run the tests with individual test output. Alternatively you can use the nox session commands above.poetryinstall
poetryrunpytest-v
Package: modelresolvers
modelresolversmodelresolvers is a python grapqhl package(inspired bytype-graphql) on top ofstrawberryto structure your project by feature(model) instead of operation type(mutation and query)
to manage it easier.Requirementpython3.10 and aboveInstall by the following commandpip install modelresolversDocumentation:https://cikay.github.io/modelresolvers/ExamplesPut the following example to main.pyimportstrawberryfrommodelresolversimportModelResolvers,Schemauser_resolvers=ModelResolvers()@strawberry.typeclassUser:firstname:strlastname:strusers=[User(firstname="John",lastname="Doe"),User(firstname="Jahe",lastname="Doe"),]@user_resolvers.query(name="user")defuser()->User:returnusers[0]@user_resolvers.mutation(name="add_user")defadd_user(firstname:str,lastname:str)->User:user=User(firstname=firstname,lastname=lastname)users.append(user)returnuserschema=Schema(models_resolvers=[user_resolvers]).strawberry_schemaRunstrawberry server mainGo tohttp://0.0.0.0:8000/graphqland try your mutations and queries.
Package: models
PyModelsis a lightweight framework for mapping Python classes to schema-less
databases. It is not an ORM as it doesn’t map existing schemata to Python
objects but instead defines them on a higher layer built upon a schema-less
storage (key/value or document-oriented). You define models as a valuable subset
of the whole database and work with only certain parts of existing entities –
the parts you need.Models were renamed to PyModelsSince 21 Feb 2010 this package is named “PyModels”. It was renamed due to name
clashes between the library and local module with concrete models. It is natural
to name the latter “models.py” (e.g. in Django), so it would obviously conflict
with an external library named “models”.Please update your bookmarks, dependency lists, etc.The package is still unstable so I hope the renaming didn’t do much harm. :)Installation$ sudo easy_install pymodels
Package: model_schemes
schemesDescriptionSchemes is a python package that allows you to easily create and manage data schemes for your data science projects.Installationpipinstallmodel_schemesUsagefrommodel_schemes.legalimportpetitiondata={...}p=petition.PetitionSchema(**data)p.model_dump()p.model_dump_json()LicenseMITAuthorRussell-AI
Package: modelscope-gradio-components
Modelscope Gradio Components✖️🤖Modelscope Studio｜ 🤗HuggingFace Space中文｜  EnglishModelScope_Gradio_Components is a set of extension component libraries based on gradio 4.x, dedicated to serving the various extension needs of gradio applications within the ModelScope Studio. It mainly focuses on enhancing conversational scenarios, supporting multimodal contexts, and providing assistance for various other specialized scenarios.Installpipinstallmodelscope_gradio_componentsQuickstartimporttimeimportgradioasgrimportmodelscope_gradio_componentsasmgrdefsubmit(_input,_chatbot):print('text：',_input.text)print('files: ',_input.files)_chatbot.append([_input,None])yield_chatbottime.sleep(1)_chatbot[-1][1]=[{"flushing":False,"text":'bot1: '+_input.text+'!'},{"text":'bot2: '+_input.text+'!'}]yield{chatbot:_chatbot,}withgr.Blocks()asdemo:chatbot=mgr.Chatbot(height=400)input=mgr.MultimodalInput()input.submit(fn=submit,inputs=[input,chatbot],outputs=[chatbot])demo.queue().launch()Component DocsThe currently supported components include:Chatbot: gradio Chatbot extension component, supports multi-modal content output, multi-bot scenarios, and custom rendering components and event interactions within the conversation content.MultimodalInput: A multi-modal input box, supporting functions such as file upload, recording, and photography.Markdown: gradio Markdown extension component, supports the output of multi-modal content (audio, video, voice, files, text).ImageGallery (WIP): gradio Gallery extension component, supports waterfall-style image display.More components are being updated...For detailed usage, seeDocumentation and ExamplesDevelopmentClone this repo locally:gitclonegit@github.com:modelscope/modelscope-gradio-components.gitcdmodelscope-gradio-components# for backendpipinstall-e'.'# for frontendnpminstallpnpm-g

pnpminstall
pnpmbuildRun demo!gradiodocs/app.pyor run a single demo like this:gradiodocs/components/Chatbot/demos/basic.py
Package: modelscope-studio
Modelscope Studio✖️GitHub| 🤖Modelscope Studio｜ 🤗Hugging Face Space中文｜  English  ｜日本語modelscope_studiois a set of extension component libraries based on gradio 4.x, dedicated to serving the various extension needs of gradio applications within the ModelScope Studio. It mainly focuses on enhancing conversational scenarios, supporting multimodal contexts, and providing assistance for various other specialized scenarios.Installpipinstallmodelscope_studioQuickstartimporttimeimportgradioasgrimportmodelscope_studioasmgrdefsubmit(_input,_chatbot):print('text：',_input.text)print('files: ',_input.files)_chatbot.append([_input,None])yield_chatbottime.sleep(1)_chatbot[-1][1]=[{"flushing":False,"text":'bot1: '+_input.text+'!'},{"text":'bot2: '+_input.text+'!'}]yield{chatbot:_chatbot,}withgr.Blocks()asdemo:chatbot=mgr.Chatbot(height=400)input=mgr.MultimodalInput()input.submit(fn=submit,inputs=[input,chatbot],outputs=[chatbot])demo.queue().launch()Component DocsThe currently supported components include:Chatbot: gradio Chatbot extension component, supports multi-modal content output, multi-bot scenarios, and custom rendering components and event interactions within the conversation content.MultimodalInput: A multi-modal input box, supporting functions such as file upload, recording, and photography.Markdown: gradio Markdown extension component, supports the output of multi-modal content (audio, video, voice, files, text).ImageGallery (WIP): gradio Gallery extension component, supports waterfall-style image display.More components...For detailed usage, seeDocumentation and ExamplesDevelopmentClone this repo locally:gitclonegit@github.com:modelscope/modelscope-studio.gitcdmodelscope-studio# for backendpipinstall-e'.'# for frontendnpminstallpnpm-g

pnpminstall
pnpmbuildRun demo!gradiodocs/app.pyor run a single demo like this:gradiodocs/components/Chatbot/demos/basic.py
Package: modelselect
modelselectA package helps easily create an optimal linear regression model by removing the insignificant and multicollinearity predictor variables, which can help you reduce the interactive process and tedious work to run the model, estimate it, evaluate it, reestimate and reevaluate it, etc.Developed by Shouke Wei from Deepsim Academy, Deepsim Intelligence Technology Inc. (c) 2022Install the packagepipinstallmodelselectimport the packagefrommodelselectimportLRSelectorthen use theLRSelector()directly. Orimportmodelselectasmsthen usems.LRSelector()DocumentAn example:https://github.com/shoukewei/modelselect/blob/main/docs/example.ipynb
Package: model-selection
Model SelectionScope of This ProjectAs a beginner in Data science/Machine learning field, most of us having issue inFeature Selection,Feature ExtractionandModel Selection. Algorithm-Finder can help you to solve this problem. using Algorithm-Finder
you can achieve the below tasks and more.Model SelectionFeature SelectionFeature ExtractionOptimized tuning parametersThis package mainly used scikit-learn for most of the estimators, by using Algorithm-Finder you can apply your dataset
on below modelsALL --> ALL INMLR --> MultiLinearRegressionPOLY --> PolynomialRegressionSVR --> SupportVectorRegressionDTREE --> DecisionTreeRegression & DecisionTreeClassificationRFR --> RandomForestRegressionSIGMOID --> LogisticRegressionKNN --> K-NearestNeighbours ClassifierSVM --> SupportVectorMachine ClassifierRFC --> RandomForestClassifierBAYESIAN --> NaiveBayesClassifier
Package: model-selector
Model-SelectionThere are a lot of predictive modeling algorithms in machine learning (ML) in engineering applications. Predictive modeling is the problem of developing a model using historical data to predict new data where we do not have the answer. Predictive modeling can be described as the mathematical problem of approximating a mapping function (f) from input variables (X) to output variables (y). This is called the problem of function approximation. Generally, we can divide all function approximation tasks into classification and regression tasks.
Regression models include Single and Multiple Linear Regression, Decision Tree, Polynomial, Random Forest, and Support Vector. Classification models include Decision Tree, K-Nearest Neighbors, Kernel SVM, Logistic Regression, NaÃ¯ve Bayes, Random Forest, and Support Vector Machine.
One of the most frequently asked questions in the data science community seeks to determine which regression or classification model is best suited to be used on different datasets. This project aims to build a python package that will help you evaluate your regression models to select the best model for your dataset quickly and efficiently. Provide the file path to your dataset with some optional parameters and watch this package do the rest of the work for you.