Saravanan666/pypi-org huggingface dataset:
Package: deepa2
Deep Argument Analysis (deepa2)This project providesdeepa2, which🥚 takes NLP data (e.g. NLI, argument mining) as ingredients;🎂 bakes DeepA2 datatsets conforming to theDeep Argument Analysis Framework;🍰 serves DeepA2 data as text2text datasets suitable for training language models.There's a public collection of 🎂 DeepA2 datatsets baked withdeepa2at theHF hub.TheDocumentationdescribes usage options and gives background info on the Deep Argument Analysis Framework.QuickstartIntegratingdeepa2into Your Training PipelineInstalldeepa2into your ML project's virtual environment, e.g.:sourcemy-projects-venv/bin/activatepython--version# should be ^3.7python-mpipinstalldeepa2Adddeepa2preprocessor to your training pipeline. Your training script may look like, for example:#!/bin/bash# configure and activate environment...# download deepa2 datasets and# prepare for text2text trainingdeepa2serve\--pathsome-deepa2-dataset\# <<< 🎂--export_formatcsv\--export_patht2t\# >>> 🍰# run default training script,# e.g., with 🤗 Transformerspython.../run_summarization.py\--train_filet2t/train.csv\# <<< 🍰--text_column"text"\--summary_column"target"\--...# clean-uprm-rt2tThat's it.Create DeepA2 datasets withdeepa2from existing NLP dataInstallpoetry.Clone the repository:gitclonehttps://github.com/debatelab/deepa2-datasets.gitInstall this package from within the repo's root folder:poetryinstallBake a DeepA2 dataset, e.g.:poetryrundeepa2bake\\--nameesnli\\# <<< 🥚--debug-size100\\--export-path./data/processed# >>> 🎂Contribute a DeepA2Builder for another DatasetWe welcome contributions to this repository, especially scripts that port existing datasets to the DeepA2 Framework. Within this repo, a code module that transforms data into the DeepA2 format containsa Builder class that describes how DeepA2 examples will be constructed and that implements the abstractbuilder.Builderinterface (such as, e.g.,builder.entailmentbank_builder.EnBankBuilder);a DataLoader which provides a method for loading the raw data as a 🤗 Dataset object (such as, for example,builder.entailmentbank_builder.EnBankLoader) -- you may usedeepa2.DataLoaderas is in case the data is available in a way compatible with 🤗 Dataset;dataclasses which describe the features of the raw data and the preprocessed data, and which extend the dummy classesdeepa2.RawExampleanddeepa2.PreprocessedExample;a collection of unit tests that check the concrete Builder's methods (such as, e.g.,tests/test_enbank.py);a documentation of the pipeline (as for example indocs/esnli.md).Considersuggestingto collaboratively construct such a pipeline by opening anew issue.CitationThis repository builds on and extends the DeepA2 Framework originally presented in:@article{betz2021deepa2,title={DeepA2: A Modular Framework for Deep Argument Analysis with Pretrained Neural Text2Text Language Models},author={Gregor Betz and Kyle Richardson},year={2021},eprint={2110.01509},archivePrefix={arXiv},primaryClass={cs.CL}}
Package: deepaas
DEEPaaSDEEP as a Service API (DEEPaaS API) is a REST API built onaiohttpthat allows to provide easy access to
machine learning, deep learning and artificial intelligence models. By using
the DEEPaaS API users can easily run a REST API in front of their model, thus
accessing its functionality via HTTP calls. DEEPaaS API leverages theOpenAPI
specification.DocumentationThe DEEPaaS documentation is hosted onRead the Docs.QuickstartThe best way to quickly try the DEEPaaS API is through:make runThis command will install a virtualenv (in thevirtualenvdirectory) with
DEEPaaS and all its dependencies and will run the DEEPaaS REST API, listening
on127.0.0.1:5000. If you browse tohttp://127.0.0.1:5000you will get the
Swagger documentation page (i.e. the Swagger web UI).Develop modeIf you want to run the code in develop mode (i.e.pip install -e), you can
issue the following command before:make developCitingIf you are using this software and want to cite it in any work, please use the
following:Lopez Garcia, A. "DEEPaaS API: a REST API for Machine Learning and
Deep Learning models". In:Journal of Open Source Software4(42) (2019),
pp. 1517. ISSN: 2475-9066. DOI:10.21105/joss.01517You can also use the following BibTeX entry:@article{Lopez2019DEEPaaS,
    journal = {Journal of Open Source Software},
    doi = {10.21105/joss.01517},
    issn = {2475-9066},
    number = {42},
    publisher = {The Open Journal},
    title = {DEEPaaS API: a REST API for Machine Learning and Deep Learning models},
    url = {http://dx.doi.org/10.21105/joss.01517},
    volume = {4},
    author = {L{\'o}pez Garc{\'i}a, {\'A}lvaro},
    pages = {1517},
    date = {2019-10-25},
    year = {2019},
    month = {10},
    day = {25},}AcknowledgementsThis software has been developed within the DEEP-Hybrid-DataCloud (Designing
and Enabling E-infrastructures for intensive Processing in a Hybrid DataCloud)
project that has received funding from the European Union's Horizon 2020
research and innovation programme under grant agreement No 777435.
Package: deep-agent
DEEP Python ClientDEEP is an open source dynamic insight engine based on the Grafana stack. The idea is to allow dynamic collection of
Traces, Metrics, Logs and Snapshots via the Grafana UI.UsageTo use DEEP simple import the package and start the agent at the earliest point in the code.pipinstalldeep-agentimportdeepdeep.start()ExamplesThere are a couple of examplesavailable here.DocumentationFor further documentation on the usage of deep-agent view thedocs.LicensingFor licensing info please seeLICENSING.md
Package: deepai
Table of ContentsDeepAIVersionTutorialNoticeInstallCircuitMethod ChainDocumentContributorsDisclaimerDeepAIDeepAIQuantum Computing SDKVersionTutorialhttps://github.com/DeepAI/DeepAI-tutorialsNoticeThe back end has been changed to tensor network. The previous backend environment can still be used with .run(backend="numpy").Installgit clone https://github.com/DeepAI/DeepAI
cd DeepAI
pip3 install -e .orpip3 install deepaiCircuitfromdeepaiimportQAOA,QML,QNNMethod ChainCircuit().h[0].x[0].z[0]c=Circuit().h[0]c.x[0].z[0]DocumentContributorsContributorsDisclaimerCopyright 2022 The DeepRecommend Developers.DeepAI
Package: deepair
Deepair is a Deep Reinforcement Learning libraryDeepair implementations of reinforcement learning algorithms. It focus on DRL algorithms and implementing the latest advancements in DRL. Highly customizable support for training processes. Suitable for the research and application of the latest technologies in reinforcement learning.FeaturesDocumentationDocumentation is available:https://deepair.readthedocs.io/Installationpip install deepairorpip install git+https://github.com/sonnhfit/deepair.gitExampleimportgymfromdeepair.dqnimportRainbowenv=gym.make('LunarLander-v2')rain=Rainbow(env=env,memory_size=10000,batch_size=32,target_update=256)rain.train(timesteps=200000)# teststate=env.reset()done=Falsescore=0whilenotdone:action=rain.select_action(state,deterministic=True)next_state,reward,done=env.step(action)state=next_statescore+=rewardprint("score: ",score)Implemented AlgorithmsTutorialHow To Contribute
Package: deep-alignment-pytorch
Copyright (c) 2019, Justus Schock
All rights reserved.Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS”
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.Description: # Deep Alignment Network PyTorch[![Build Status](https://travis-ci.com/justusschock/deep_alignment_network_pytorch.svg?branch=master)](https://travis-ci.com/justusschock/deep_alignment_network_pytorch) [![codecov](https://codecov.io/gh/justusschock/deep_alignment_network_pytorch/branch/master/graph/badge.svg)](https://codecov.io/gh/justusschock/deep_alignment_network_pytorch)This repository contains a [delira](https://github.com/justusschock/delira)-compatibleimplementation of the Deep Alignment Network.The original implementation can be found [here](https://github.com/MarekKowalski/DeepAlignmentNetwork) and the paper is available on [arXiv](https://arxiv.org/abs/1706.01789)An example using the HELEN dataset can be found in the [notebooks](notebooks/felen_example.ipynb) folder.## InstallationThis repository can be installed from pip via:` pip installdeep-alignment-pytorch`or from source via` pip installgit+https://github.com/justusschock/deep_alignment_network_pytorch`An installation from pip will be possible soon, once the complete repository setup is completed.## Poster (taken from original implementation)
<img src=”http://home.elka.pw.edu.pl/~mkowals6/lib/exe/fetch.php?media=wiki:dan-poster.jpg” width=”60%”>Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Package: deep-animator
Deep AnimatorImage animation given a driving video sequence and a source image.Deep Animator is an implementation ofFirst Order Motion Model for Image AnimationbyAliaksandr Siarohin,Stéphane Lathuilière,Sergey Tulyakov,Elisa RicciandNicu Sebe.The source code can be foundhere.This library is a simple transformation of the original code into a shell script for quick experimentation. This is also an educational effort for the writer.InstallRunpip install deep-animatorto install the library in your environment.How to useFirst you need to download the weights of the modelhere. Then just run the following command.deep_animate <path_to_the_source_image> <path_to_the_driving_video> <path_to_yaml_conf> <path_to_model_weights>Example of source imagehereExample of driving videohereAn example YAML file is given below:model_params:
  common_params:
    num_kp: 10
    num_channels: 3
    estimate_jacobian: True
  kp_detector_params:
     temperature: 0.1
     block_expansion: 32
     max_features: 1024
     scale_factor: 0.25
     num_blocks: 5
  generator_params:
    block_expansion: 64
    max_features: 512
    num_down_blocks: 2
    num_bottleneck_blocks: 6
    estimate_occlusion_map: True
    dense_motion_params:
      block_expansion: 64
      max_features: 1024
      num_blocks: 5
      scale_factor: 0.25
  discriminator_params:
    scales: [1]
    block_expansion: 32
    max_features: 512
    num_blocks: 4
    sn: True
Package: deepapi
Deep APIDeep Learning as Cloud APIs.This project provides an image classification cloud service for research onBlack-box Adversarial Attacks.Quick StartUsing Docker:docker run -p 8080:8080 wuhanstudio/deepapiPython 3:$ pip install deepapi

# For development
# python setup.py develop

# By default, we enable all models on the server.
# Use deepapi -h to see more options.

$ python -m deepapi
Serving on port 8080...The website and API service are available athttps://localhost:8080.DeepAPI ClientTo initiateblack-box adversarial attacks, we can get predictions from a cloud API usingmodel.predict().Behind the scene, thismodelmakes predictions by sending a POST request tohttp://localhost:8080/vgg16_cifar10.import numpy as np
from PIL import Image

from deepapi.api import DeepAPI_VGG16_Cifar10

# Load the image
x = Image.open("dog.jpg")
x = np.array(x)

# Initialize the model
model  = DeepAPI_VGG16_Cifar10('http://localhost:8080', concurrency=8)

# Predict
y = model.predict(np.array([x]))[0]

# Print the result
model.print(y)Using Curlexport IMAGE_FILE=test/cat.jpg
(echo -n '{"file": "'; base64 $IMAGE_FILE; echo '"}') | \
curl -H "Content-Type: application/json" \
     -d @- http://127.0.0.1:8080/vgg16_cifar10Using Python RequestYou can also implement the API client from scratch using the request module.def classification(url, file):
    # Load the input image and construct the payload for the request
    image = Image.open(file)
    buff = BytesIO()
    image.save(buff, format="JPEG")

    data = {'file': base64.b64encode(buff.getvalue()).decode("utf-8")}
    return requests.post(url, json=data).json()

res = classification('http://127.0.0.1:8080/vgg16_cifar10', 'cat.jpg')This python script is available in thetestfolder. You should see prediction results by runningpython3 minimal.py:cat            0.99804
deer           0.00156
truck          0.00012
airplane       0.00010
dog            0.00009
bird           0.00005
ship           0.00003
frog           0.00001
horse          0.00001
automobile     0.00001Concurrent requestsSending 5 concurrent requests to the API server:$ python3 multi-client.py --num_workers 5 cat.jpgYou should see the result:----- start -----
Sending requests
Sending requests
Sending requests
Sending requests
Sending requests
------ end ------
Concurrent Requests: 5
Total Runtime: 2.441638708114624
Package: deepaste
DPASTE -- a CLI paste toolThis script can help you paste content from the CLI onto a web service atdpaste.de.UsagePaste one linersdpaste --content <content> --expire <time> --lexer <content_type>Pase a file contentdpaste --file <file> --expire <time> --lexer <content_type>Using a pipe to send pastesYou can also use a pipe to send text to dpaste, for examplejournalctl -b | dpasteWhen successful, the link to the pasted content will be printed to the console.VariablesExpiration timeThis variable sets how long the page will keep your snippet. You can use:onetime (This will let you display once.)hour (default)dayweekContent type (lexer)This variable tells the engine how to highlight the content._text (default)bashchtmlperlpythonjavarsttexvimMore can be seenin the Dpaste documentaton.ExampleWhen you have a python scriptprogram.pyand you want to paste it for aweek, withPythonhighlighting, do:dpaste --file program.py --expire week --lexer pythonYou can also use-f,-e, and-lshortcuts to provide arguments.
Package: deepauth
To use captcha, install the following packages:sudoaptinstalllibz-devlibjpeg-devlibfreetype6-devpython-devTo install, adddeepauthandcaptchatoINSTALLED_APPSin your Django settings.Compulsory configurations:AUTH_USER_MODEL='deepauth.Account'DEEPAUTH_EMAIL_CONF={'required':True,'server':'smtp.example.com','port':465,'username':'noreply@example.com','password':'moc.elpmaxe','subject':'Activate Your Account','content':'Dear{0},\nPlease verify your account by clicking the following link:\n{1}\nYours sincerely,\nExample.com',}Optional configurations:DEEPAUTH_INVITATION_ONLY=False# Invatitation code must be provided if set to true.DEEPAUTH_AUTO_LOGIN=False# User will be logged in after certain actions (currently only work for updating email).TOKEN_LIFETIME=7# Token will be expired after certain days.To enable access, add the following URLs to your URL patterns:url(r'^auth/',include('deepauth.urls')),url(r'^captcha/',include('captcha.urls'))To show docs, add the following code to your URLs:fromrest_framework.documentationimportinclude_docs_urlsurlpatterns=[url(r'^docs/',include_docs_urls('API Docs'))]
Package: deepbay
DeepBayThis project was created with the objective of compile Machine Learning Architectures created using Tensorflow or Keras. The architectures must be provided as a ready-to-use Plug-and-Play module that can be easily integrated into any existing project or architecture designInstallationYou can use pip for install this from PyPi, just use:pip install deepbayhttps://pypi.org/project/deepbay
Package: deepbayes
deepbayesdeepbayes is a package that aims to make training Bayesian neural networks (BNNs) simple.Implemented on top of TensorFlow 2.0 and working with tf.keras as a primary interface, one can simply declare a keras (or TF) model and pass it to
the desired deepbayes optimizer and it will compile the model into an object that is ready to perform approximate inference.deepbayes is actively under development, and location and names of packages may change (such changes will be noted at the bottom of this repo).  jax support coming soon :)Install:pip install deepbayesSupported OptimizersBayesByBackpropImplemented based on description inhttps://arxiv.org/abs/1505.05424Hamiltonian (Hybrid) Monte CarloImplemented based on description inhttps://arxiv.org/abs/1206.1901NoisyAdam (aka Vadam)Implemented based on description inhttps://arxiv.org/abs/1712.02390Variational Online Gauss Newton (aka VOGN)Implemented based on description inhttps://arxiv.org/abs/1806.04854andhttps://arxiv.org/abs/2002.10060Stochastic Weight Averaging - Gaussian (aka SWAG)Implemented bsed on description inhttps://arxiv.org/pdf/1902.02476.pdfStochastic Gradient DescentNot sure what to reference you to here... a google will sufficeSupported AnalysisIn addition to producing posterior distributions for BNNs, deepbayes also supports some basic analysis includingAdversarial attacks for BNNs (FGSM, PGD, CW)Uncertainty quantificationStatistical guarentees of robustness -https://arxiv.org/pdf/1903.01980.pdfProbabalistic safety estimates  -https://arxiv.org/pdf/2004.10281.pdfDependancies:tensorflow, tensorflow-probability, numpy, tqdm, statsmodels,Future SupportBelow we have a tentative to-do list of inference methods to impliment (7 to be exact) and other properties we want to tool to have.HTML documentationNon-Gaussian Posterior (requires extension of posterior representation as well)Stochastic Variational Inference (SVI)Probabilistic Backprop (PBP)Sequential Monte Carlo optimizer (SMC)Monte Carlo Dropout optimizer (MCD)Stochastic Gradient Langevin Dynamics optimizer (SGLD)Stochastic Gradient Markov Chain Monte Carlo (SGMCMC)Riemann manifold HMC (RMHMC)JAX autodif library instead of TF (deepbayes.jax.optimizers)Binary Bayesian neural networks
Package: deepbet
This is the official implementation of thedeepbet paper.deepbet is a neural network based tool which achieves state-of-the-art results for brain extraction of T1w MR images
of healthy adults while taking ~1 second per image.UsageAfter installation, there are three ways to use deepbetdeepbet-guiruns theGraphical User Interface (GUI)deepbet-cliruns theCommand Line Interface (CLI)Run deepbet directly in Pythonfromdeepbetimportrun_betinput_paths=['path/to/sub_1/t1.nii.gz','path/to/sub_2/t1.nii.gz']brain_paths=['path/to/sub_1/brain.nii.gz','path/to/sub_2/brain.nii.gz']mask_paths=['path/to/sub_1/mask.nii.gz','path/to/sub_2/mask.nii.gz']tiv_paths=['path/to/sub_1/tiv.csv','path/to/sub_2/tiv.csv']run_bet(input_paths,brain_paths,mask_paths,tiv_paths,threshold=.5,n_dilate=0,no_gpu=False)Besides theinput pathsand the output pathsbrain_paths: Destination filepaths of input niftifiles with brain extraction appliedmask_paths: Destination filepaths ofbrain mask nifti filestiv_paths: Destination filepaths of.csv-files containing the total intracranial volume (TIV)in cm³Simpler than it sounds: TIV = Voxel volume * Number of 1-Voxels in brain maskyou can additionally doFine adjustmentsviathreshold: deepbet internally predicts values between 0 and 1 for each voxel and then includes each voxel which is above 0.5.
You can change this threshold (e.g. to 0.1 to include more voxels).Coarse adjustmentsvian_dilate: Enlarges/shrinks mask by successively adding/removing voxels adjacent to mask surface.and choose if you want touse GPU (NVIDIA and Apple M1/M2 support) for speedupno_gpu: deepbet automatically uses NVIDIA GPU or Apple M1/M2 if available. If you do not want that set no_gpu=True.Installationpipinstalldeepbet
condainstall-canacondapyqt=5.15.7CitationIf you find this code useful in your research, please consider citing:@inproceedings{deepbet,
Author = {Lukas Fisch, Stefan Zumdick, Carlotta Barkhau, Daniel Emden, Jan Ernsting, Ramona Leenings, Kelvin Sarink, Nils R. Winter, Udo Dannlowski, Tim Hahn},
Title = {fastbet: Fast brain extraction of T1-weighted MRI using Convolutional Neural Networks},
Journal  = {Imaging Neuroscience},
Year = {2023}
}
Package: deepbiome
Deep Learning package using the phylogenetic tree information for microbiome abandunce data analysis.Free software: 3-clause BSD licenseDocumentation:https://Young-won.github.io/deepbiomeInstallationPrerequisitespython >= 3.5TensorflowKerasInstall DeepBiomeAt the command line:# for python 3.x$pip3installgit+https://github.com/Young-won/deepbiome.gitFeaturesdeepbiome.deepbiome_train(log, network_info, path_info, number_of_fold=None, max_queue_size=10, workers=1, use_multiprocessing=False)Function for training the deep neural network with phylogenetic tree weight regularizer.It uses microbiome abundance data as input and uses the phylogenetic taxonomy to guide the decision of the optimal number of layers and neurons in the deep learning architecture.deepbiome.deepbiome_test(log, network_info, path_info, number_of_fold=None, max_queue_size=10, workers=1, use_multiprocessing=False)Function for testing the pretrained deep neural network with phylogenetic tree weight regularizer.If you use the index file, this function provide the evaluation using test index (index set not included in the index file) for each fold. If not, this function provide the evaluation using the whole samples.deepbiome.deepbiome.deepbiome_prediction(log, network_info, path_info, num_classes, number_of_fold=None, max_queue_size=10, workers=1, use_multiprocessing=False)Function for prediction by the pretrained deep neural network with phylogenetic tree weight regularizer.CreditsThis package was builded on theKerasand theTensorflowpackages.This package was created withCookiecutterand theNSLS-II/scientific-python-cookiecutterproject template.
Package: deepblink
In biomedical microscopy data, a common task involves the detection of diffraction-limited spots thatvisualize single proteins, domains, mRNAs, and many more. These spots were traditionally detected withmathematical operators such as Laplacian of Gaussian. These operators, however, rely on human input rangingfrom image-intensity thresholds, approximative spot sizes, etc. This process is tedious and not alwaysreliable.DeepBlink relies on neural networks to automatically find spots without the need for humanintervention. DeepBlink is available as a ready-to-use command-line interface.All deepBlink wheels distributed on PyPI are MIT licensed.
Package: deepblocks
Deep-Learning-BlocksA library with customized PyTorch layers and model components.InstallDocumentationWhat's availableBug/FeatureInstall:To install the latest stable version:pip install deepblocksFor a specific version:pip install deepblocks==0.1.9To install the latest, but unstable version:pip install git+https://github.com/blurry-mood/Deep-Learning-BlocksWhat's available:NetworksConvMixerU-NetICT-NetLayersConvMixer LayerFlip-Invariant Conv2dSqueeze-Excitation BlockDense BlockMulti-Head Self-AttentionMulti-Head Self-Attention V2ActivationsFunnel ReLULoss FunctionsFocal LossAUC LossAUC Margin LossKL Divergence LossRegularization functionsAnti-CorrelationSelf-supervised LearningBarlow TwinDINOOptimizersSAMDocumentation:The current documention is hostedhereBug or Feature:Deepblocks is a growing package. If you encounter abugor would like to request afeature, please feel free to open an issuehere.