Saravanan666/pypi-org huggingface dataset:
Package: glum
glumDocumentationGeneralized linear models (GLM) are a core statistical tool that include many common methods like least-squares regression, Poisson regression and logistic regression as special cases. At QuantCo, we have used GLMs in e-commerce pricing, insurance claims prediction and more. We have developedglum, a fast Python-first GLM library. The development was based ona fork of scikit-learn, so it has a scikit-learn-like API. We are thankful for the starting point provided by Christian Lorentzen in that PR!The goal ofglumis to be at least as feature-complete as existing GLM libraries likeglmnetorh2o. It supportsBuilt-in cross validation for optimal regularization, efficiently exploiting a “regularization path”L1 regularization, which produces sparse and easily interpretable solutionsL2 regularization, including variable matrix-valued (Tikhonov) penalties, which are useful in modeling correlated effectsElastic net regularizationNormal, Poisson, logistic, gamma, and Tweedie distributions, plus varied and customizable link functionsBox constraints, linear inequality constraints, sample weights, offsetsThis repo also includes tools for benchmarking GLM implementations in theglum_benchmarksmodule. For details on the benchmarking,see here. Although the performance ofglumrelative toglmnetandh2odepends on the specific problem, we find that when N >> K (there are more observations than predictors), it is consistently much faster for a wide range of problems.For more information onglum, including tutorials and API reference, please seethe documentation.Why did we choose the nameglum? We wanted a name that had the letters GLM and wasn't easily confused with any existing implementation. And we thought glum sounded like a funny name (and not glum at all!). If you need a more professional sounding name, feel free to pronounce it as G-L-um. Or maybe it stands for "Generalized linear... ummm... modeling?"A classic example predicting housing prices>>>fromsklearn.datasetsimportfetch_openml>>>fromglumimportGeneralizedLinearRegressor>>>>>># This dataset contains house sale prices for King County, which includes>>># Seattle. It includes homes sold between May 2014 and May 2015.>>>house_data=fetch_openml(name="house_sales",version=3,as_frame=True)>>>>>># Use only select features>>>X=house_data.data[...[..."bedrooms",..."bathrooms",..."sqft_living",..."floors",..."waterfront",..."view",..."condition",..."grade",..."yr_built",..."yr_renovated",...]...].copy()>>>>>>>>># Model whether a house had an above or below median price via a Binomial>>># distribution. We'll be doing L1-regularized logistic regression.>>>price=house_data.target>>>y=(price<price.median()).values.astype(int)>>>model=GeneralizedLinearRegressor(...family='binomial',...l1_ratio=1.0,...alpha=0.001...)>>>>>>_=model.fit(X=X,y=y)>>>>>># .report_diagnostics shows details about the steps taken by the iterative solver>>>diags=model.get_formatted_diagnostics(full_report=True)>>>diags[['objective_fct']]objective_fctn_iter00.69309110.48950020.44958530.44368140.44349850.443497InstallationPlease install the package through conda-forge:condainstallglum-cconda-forge
Package: gluon
A Model-Driven, Extensible Framework for Networking APIs
Gluon provides a framework for specifying, using a model file,
APIs for network forwarding.  It is intended to offer developers
the possibility of quickly prototyping any networking forwarding
system that describes how a packet moves from port to port or
port to the outside world.Free software: Apache licenseWiki:https://wiki.openstack.org/wiki/GluonSource:http://git.openstack.org/cgit/openstack/gluonBugs:https://bugs.launchpad.net/python-gluonDocumentation: doc/source/indexInstallation: doc/source/installationUser Guide: doc/source/usageDevelopers: doc/source/devref/index
Package: gluon2keras
gluon2kerasGluon to Keras deep neural network model converterInstallationgit clone https://github.com/nerox8664/gluon2keras
cd gluon2keras
pip install -e .or you can usepip:pip install gluon2kerasUsage# Get a model from gluon cv
from gluoncv2.model_provider import get_model as glcv2_get_model
net = glcv2_get_model("xception")

# Make sure it's hybrid and initialized
net.hybridize()
net.collect_params().initialize()

# Test input
input_np = np.random.uniform(0, 1, (1, 3, 299, 299))
gluon_output = net(mx.nd.array(input_np))

# Keras model
k_model = gluon2keras(net, [(1, 3, 299, 299)], verbose=True, names='short')
error = check_error(gluon_output, k_model, input_np)Code snippetsLook at thetestsdirectory.LicenseThis software is covered by MIT License.
Package: gluonar
Gluon Audio is a toolkit providing deep learning based audio recognition
algorithm. The project is still under development, and only Chinese
introduction will be provided.GluonAR Introduction:GluonAR is based on MXnet-Gluon, if you are new to it, please check outdmlc 60-minute crash course.虽然名字叫GluonAR, 但是目前以及可以预见的时间内只有Text-Independent
Speaker Recognition的内容.已经实现的feature: - 使用ffmpeg的pythonic bindingav和librosa做audio数据读取 - 模块支持Hybridize().
forward阶段不使用pysound, librosa, scipy, 效率更高,
提供快速训练和end-to-end部署的能力, 包括: -
基于nd.contrib.fft的短时傅里叶变换(STFTBlock)和z-score
block, 相比使用numpy和scipy预处理后载入GPU训练效率提高12%. -MelSpectrogram,DCT1D,MFCC,PowerToDB-1808.00158中提出的SincBlock- gluon风格的VOX数据集载入 - 类似人脸验证的Speaker Verification -
使用频谱图训练声纹特征的例子, 在VOX1上的1:1验证acc: 0.941152+-0.004926example:importnumpyasnpimportmxnetasmximportlibrosaasrosafromgluonar.utils.vizimportview_specfromgluonar.nn.basic_blocksimportSTFTBlockdata=rosa.load(r"resources/speaker_recognition/speaker0_0.m4a",sr=16000)[0][:35840]nd_data=mx.nd.array([data],ctx=mx.gpu())stft=STFTBlock(35840,hop_length=160,win_length=400)stft.initialize(ctx=mx.gpu())# stft block forwardret=stft(nd_data).asnumpy()[0][0]spec=np.transpose(ret,(1,0))**2view_spec(spec)# stft in librosaspec=rosa.stft(data,hop_length=160,win_length=400,window="hamming")spec=np.abs(spec)**2view_spec(spec)输出:STFTBlockSTFT in librosa更多的例子请参考examples/.Requirementsmxnet-1.5.0+, gluonfr, av, librosa, …音频库的选择主要考虑数据读取速度,
训练过程中音频的解码相比图像解码会消耗更多时间,
实际测试librosa从磁盘加载一个aac编码的短音频 耗时是pyav的8倍左右.librosapip install librosaffmpeg# 下载ffmpeg源码, 进入根目录
./configure --extra-cflags=-fPIC --enable-shared
make -j
sudo make installpyav, 需要先安装ffmpegpip install avgluonfrpip installgit+https://github.com/THUFutureLab/gluon-face.git@masterDatasetsTIMITThe DARPA TIMIT Acoustic-Phonetic Continuous Speech Corpus (TIMIT)
Training and Test Data. Before using this dataset please follow the
instruction onlink.A copy of this was uploaded toGoogle Driveby @philipperemyhere.VoxCelebVoxCeleb is an audio-visual dataset consisting of short clips of human
speech, extracted from interview videos uploaded to YouTube.For more information, checkout thispage.Pretrained ModelsSpeaker RecognitionResNet18 training with VoxCelebDownload:Baidu,Google
DriveI followed the ideas in paperVoxCeleb21806.05622to train this model,
the differences between them:Res18
in
this
repoRes34
in
paperTrain
ed
onVoxCel
eb2VoxCel
eb2Input
spec
size224x22
4512x30
0Eval
onRandom
9500+
pair
sample
s
from
VoxCel
eb1
train
and
test
setOrigin
al
VoxCel
eb1
test
setMetri
cAccura
cy:0.9
32656+
-0.005
187EER:
0.0504Frame
workMxnet
GluonMatcon
vnetROCTODO接下来会慢慢补全使用mxnet gluon训练说话人识别的工具链, 预计会花超长时间.DocsGluonAR documentation is not available now.Authors{haoxintong}DiscussionAny suggestions, please open an issue.ContributesThe final goal of this project is providing an easy using deep learning
based audio algorithm library likepytorch-kaldi.Contribution is welcomed.ReferencesMXNet Documentation and Tutorialshttps://zh.diveintodeeplearning.org/
Package: gluoncv
GluonCVprovides implementations of the state-of-the-art (SOTA) deep learning models in computer vision.It is designed for engineers, researchers, and students to fast prototype products and research ideas based on these models.InstallationTo install, use:pipinstallgluoncvmxnet>=1.6.0--upgrade# for installing gluoncv with all dependenciespipinstallgluoncv[full]mxnet>=1.6.0--upgradeTo enable different hardware supports such as GPUs, check outmxnet variants.For example, you can install cuda-11.0 supported mxnet alongside gluoncv:pipinstallgluoncvmxnet-cu110>=1.6.0--upgrade
Package: gluoncv-torch
PyTorch-EncodingGluonCV-TorchLoadGluonCVModels in PyTorch.
Simplyimport gluoncvthto getting better pretrained model thantorchvision:importgluoncvthasgcvmodel=gcv.models.resnet50(pretrained=True)Installation:pipinstallgluoncv-torchAvailable ModelsImageNetImageNet models single-crop error rates, comparing to thetorchvisionmodels:torchvisiongluoncvthModelTop-1 errorTop-5 errorTop-1 errorTop-5 errorResNet1830.2410.9229.0610.17ResNet3426.708.5825.357.92ResNet5023.857.1322.336.18ResNet10122.636.4420.805.39ResNet15221.695.9420.565.39Inception v322.556.4421.335.61More models available atGluonCV Image Classification ModelZooSemantic SegmentationResults on Pascal VOC dataset:ModelBase NetworkmIoUFCNResNet10183.6PSPNetResNet10185.1DeepLabV3ResNet10186.2Results on ADE20K dataset:ModelBase NetworkPixAccmIoUFCNResNet10180.641.6PSPNetResNet10180.842.9DeepLabV3ResNet10181.144.1Quick Demoimporttorchimportgluoncvth# Get the modelmodel=gluoncvth.models.get_deeplab_resnet101_ade(pretrained=True)model.eval()# Prepare the imageurl='https://github.com/zhanghang1989/image-data/blob/master/encoding/'+\'segmentation/ade20k/ADE_val_00001142.jpg?raw=true'filename='example.jpg'img=gluoncvth.utils.load_image(gluoncvth.utils.download(url,filename)).unsqueeze(0)# Make predictionoutput=model.evaluate(img)predict=torch.max(output,1)[1].cpu().numpy()+1# Get color pallete for visualizationmask=gluoncvth.utils.get_mask_pallete(predict,'ade20k')mask.save('output.png')More models available atGluonCV Semantic Segmentation ModelZooAPI ReferenceResNetgluoncvth.models.resnet18(pretrained=True)gluoncvth.models.resnet34(pretrained=True)gluoncvth.models.resnet50(pretrained=True)gluoncvth.models.resnet101(pretrained=True)gluoncvth.models.resnet152(pretrained=True)FCNgluoncvth.models.get_fcn_resnet101_voc(pretrained=True)gluoncvth.models.get_fcn_resnet101_ade(pretrained=True)PSPNetgluoncvth.models.get_psp_resnet101_voc(pretrained=True)gluoncvth.models.get_psp_resnet101_ade(pretrained=True)DeepLabV3gluoncvth.models.get_deeplab_resnet101_voc(pretrained=True)gluoncvth.models.get_deeplab_resnet101_ade(pretrained=True)WhyGluonCV?1. State-of-the-art Implementations2. Pretrained Models and Tutorials3. Community SupportWe expect this PyTorch inference API for GluonCV models will be beneficial to the entire computer vision comunity.
Package: gluonfr
Gluon FR ToolkitGluonFR is a toolkit based on MXnet-Gluon, provides SOTA deep learning algorithm and models in face recognition.InstallationGluonFR supports Python 3.5 or later.
To install this package you need install GluonCV and MXNet first:pipinstallgluoncv--pre
pipinstallmxnet-mkl--pre--upgrade# if cuda XX is installedpipinstallmxnet-cuXXmkl--pre--upgradeThen install gluonfr:From Source(recommend)pipinstallgit+https://github.com/THUFutureLab/gluon-face.git@masterPippipinstallgluonfrGluonFR Introduction:GluonFR is based on MXnet-Gluon, if you are new to it, please check outdmlc 60-minute crash course.Data:This part provides input pipeline for training and validation,
all datasets is aligned by mtcnn and cropped to (112, 112) by DeepInsight,
they converted images totrain.rec,train.idxandval_data.binfiles,  please check out[insightface/Dataset-Zoo]for more information.
Indata/dali_utils.py, there is a simple example of Nvidia-DALI. It is worth trying when data augmentation with cpu
can not satisfy the speed of gpu training,The files should be prepared like:face/
    emore/
        train.rec
        train.idx
        property
    ms1m/
        train.rec
        train.idx
        property
    lfw.bin
    agedb_30.bin
    ...
    vgg2_fp.binWe use~/.mxnet/datasetsas default dataset root to match mxnet setting.Model_Zoo:mobile_facenet, res_attention_net, se_resnet...Loss:GluonFR provides implement of losses in recent, including SoftmaxCrossEntropyLoss, ArcLoss, TripletLoss,
RingLoss, CosLoss, L2Softmax, ASoftmax, CenterLoss, ContrastiveLoss, ... , and we will keep updating in future.If there is any method we overlooked, please open anissue.Example:examples/shows how to use gluonfr to train a face recognition model, and how to get Mnist 2-D
feature embedding visualization.Losses in GluonFR:The last column of this chart is the best LFW accuracy reported in paper, they are trained with different data and networks,
later we will give our results of these method with same train data and network.MethodPaperVisualization of MNISTLFWContrastive LossContrastiveLoss--Triplet1503.03832-99.63±0.09Center LossCenterLoss99.28L2-Softmax1703.09507-99.33A-Softmax1704.08063-99.42CosLoss/AMSoftmax1801.05599/1801.0559999.17Arcloss1801.0769899.82Ring loss1803.0013099.52LGM Loss1803.0298899.20±0.03Pretrained ModelsSeeModel Zooin doc.TodoMore pretrained modelsIJB and Megaface ResultsOther lossesDataloader for loss depend on how to provide batches like Triplet, ContrastiveLoss, RangeLoss...Try GluonCV resnetV1b/c/d/ to improve performanceCreate hosted docsTest modulePypi packageDocsPlease checkoutlink.For Chinese Version:linkAuthors{haoxintongYangxvHaoyadongSunhao}Discussion中文社区Gluon-ForumFeel free to use English here :D.ReferencesMXNet Documentation and Tutorialshttps://zh.diveintodeeplearning.org/NVIDIA DALI documentationNVIDIA DALI documentationDeepinsightinsightface
Package: gluonts-extend
# gluonts_extend#### 介绍
使用gluonts实现的当前比较流行的时间序列算法。#### 软件架构
软件架构说明#### 安装教程xxxxxxxxxxxx#### 使用说明xxxxxxxxxxxx#### 参与贡献Fork 本仓库新建 Feat_xxx 分支提交代码新建 Pull Request#### 特技使用 Readme_XXX.md 来支持不同的语言，例如 Readme_en.md, Readme_zh.mdGitee 官方博客 [blog.gitee.com](https://blog.gitee.com)你可以 [https://gitee.com/explore](https://gitee.com/explore) 这个地址来了解 Gitee 上的优秀开源项目[GVP](https://gitee.com/gvp) 全称是 Gitee 最有价值开源项目，是综合评定出的优秀开源项目Gitee 官方提供的使用手册 [https://gitee.com/help](https://gitee.com/help)Gitee 封面人物是一档用来展示 Gitee 会员风采的栏目 [https://gitee.com/gitee-stars/](https://gitee.com/gitee-stars/)
Package: gluster-stats
Gluster Statisticsgluster-stats is a command-line utility that generates health-related gluster
statistics in json, for use in monitoring gluster.Installationpip installgluster-statsUsageReturn gluster stats in json format. Requires root privileges.Options:$ gluster-stats --help
usage: gluster-stats [-h] [--record] [--version] [--timeout TIMEOUT]

Collect stats related to gluster

optional arguments:
  -h, --help         show this help message and exit
  --record           Record the gluster cli responses in a local response file
  --version          show program's version number and exit
  --timeout TIMEOUT  Timeout per command in seconds. Defaults to 300.Example output:$ sudo gluster-stats
{
 "brick_stats": {
  "preprodcomms": [
   {
    "disk_free": 982151646412,
    "disk_total": 1064615018496,
    "disk_usage_percent": "0.08",
    "disk_used": 82463372084,
    "inode_free": 206325202,
    "inode_total": 208035840,
    "inode_usage_percent": "0.01",
    "inode_used": 1710638,
    "online": 1
   },
   {
    "disk_free": 984191755878,
    "disk_total": 1064615018496,
    "disk_usage_percent": "0.08",
    "disk_used": 80423262618,
    "inode_free": 206325528,
    "inode_total": 208035840,
    "inode_usage_percent": "0.01",
    "inode_used": 1710312,
    "online": 1
   },
   {
    "disk_free": 985802368614,
    "disk_total": 1064615018496,
    "disk_usage_percent": "0.07",
    "disk_used": 78812649882,
    "inode_free": 206325083,
    "inode_total": 208035840,
    "inode_usage_percent": "0.01",
    "inode_used": 1710757,
    "online": 1
   }
  ]
 },
 "gluster_version": "3.7.8",
 "glusterd": 1,
 "glusterfsd": 1,
 "peers": 2,
 "split_brain_entries": {
  "preprodcomms": 0
 },
 "unhealed_entries": {
  "preprodcomms": 855
 },
 "volume_count": 1,
 "volumes": [
  "preprodcomms"
 ]
}CommandsThe following commands are run and parsed to generate the json output:gluster--versiongluster peer statusgluster volume listpidof glusterdpidof glusterfsdFor each volume, the following commands are run:gluster volume heal <volume> infogluster volume heal <volume> infosplit-braingluster volume status <volume> detailWhen run with –sudo, the gluster commands that require sudo access (all but
–version) are run with sudo prepended.TestingRunmake testTests are run locally with generated mock response files. Rungluster-stats-generateto generate new mock response file. The file will be
saved at gluster_stats_<gluster_version>.json.Versioninggluster-stats uses semantic versioning. Backward incompatible changes to the
json output (such as changing something from a list to a dict) require a major
version bump. New values in a dictionary will come with a minor version bump.
Bug fixes and patches will use a patch bump.TodoUse the secret –xml option withgluster volume status- status was hard
to parse and the parser is probably fragile. This will require new mock test
files.Additional health checks?History0.0.1 (2016-05-26)First release
Package: glutamate
glutamateEasy to use Python library for querying and downloading posts from e621.net.InstallationpipinstallglutamateExamplefrompathlibimportPathfromglutamate.databaseimportE621,E621Data,E621PostsCSV,E621TagsCSV,Query,autoinit_from_directoryfromglutamate.datasetimportget_captions,write_captions,write_statsfromglutamate.downloadimportdownload_posts# Init with qualified file pathse621_data_directory=Path('./e621-data/')posts_csv=e621_data_directory/'posts.csv'posts=E621PostsCSV(posts_csv)tags_csv=e621_data_directory/'tags.csv'tags=E621TagsCSV(tags_csv)e621:E621=E621Data(posts,tags)# or simple automatic init with directory contains CSVse621_data_directory=Path('./e621-data/')e621=autoinit_from_directory(e621_data_directory)query=Query(("kisha","solo"))kisha_dataset=e621.select(query)target_directory=Path().cwd()/'tmp'/'kisha_solo'target_directory.mkdir(parents=True,exist_ok=True)results=download_posts(posts,target_directory,naming='id')failed=[resultforresultinresultsifnotresult.ok]iffailed:print(f"Failed to download{len(failed)}posts")captions=kisha_dataset.get_captions(naming='id',remove_underscores=True,tags_to_head=('kisha','kisha (character)'))write_captions(captions,target_directory)counts_csv=target_directory/'tags.csv'counts=kisha_dataset.get_tags_stats()write_stats(counts,counts_csv)
Package: gluttony_tw
# Time-To-Eat 今天吃什麼 [![Build Status](https://travis-ci.org/Stufinite/Time-To-Dinner.svg?branch=master)](https://travis-ci.org/Stufinite/Time-To-Dinner)* 這是一個改變外食模式的一個專案，結合Crawler、arduino、raspberry pi、APP等元素，希望改善學生吃飯的不便* [Crawler](Crawler)* [arduino](arduino)* [raspberryPi](raspberryPi)install : `sudo apt-get install libtiff5-dev libjpeg8-dev zlib1g-dev \libfreetype6-dev liblcms2-dev libwebp-dev tcl8.6-dev tk8.6-dev python-tk`
Package: gluware-ansible-inventory
Gluware Ansible Inventory (Inventory plugin and Gluware Ansible modules)This python package installs a Ansible Inventory plugin (glu_devices) to interact with a Gluware Control system to get the current network devices of a organization.  A "inventory" user can be created in the Gluware Control system that can then be used in the configuration of the glu_devices inventory plugin to access the devices of the Gluware Control system.The glu_devices Ansible Inventory plugin gives Ansible playbooks the ability to connect to Gluware devices and get the "ansible_network_os" property of the device to know what kind of device it is (if the discovery function in Gluware is used).  The glu_devices inventory plugin also exposes to Ansible playbooks any custom Gluware device attributes that start with "ansible_var_" (minus the "ansible_var_" part) to act on within the logic of the playbook.  In addition, the other Gluware device attributes can be mapped to Ansible variables through configuration settings of the glu_devices.In version 1.2, Gluware Ansible modules are also installed to allow interaction with Gluware device data from within playbook tasks. The Inventory plugin was updated to supply the 'glu_device_id' for the Gluware Ansible modules. The modules are identified as follows:glu_audit_configPerform a audit on the current captured config on a Gluware Deviceglu_capture_configPerform a capture config on a Gluware Deviceglu_run_discover_device_attributesPerform a discover device attributes on a Gluware Deviceglu_update_device_attributesUpdate device attributes on a Gluware DeviceTo install on the system that is running Ansible run the command line: "pip install gluware-ansible-inventory".  If updating to a later version the run the command line: "pip install -I gluware-ansible-inventory".After install run the command line: "ansible-doc -t inventory glu_devices" to see the specifics of how to use the glu_devices inventory plugin to connect to the Gluware Control system.  To see the documentation for each module run the command line: "ansible-doc -t module {{ module_name }}".Install Command$ pip install gluware-ansible-inventoryUpdate Command:$ pip install -I gluware-ansible-inventoryUsage documentation command after install$ ansible-doc -t inventory glu_devices$ ansible-doc -t module glu_audit_config$ ansible-doc -t module glu_capture_config$ ansible-doc -t module glu_run_discover_device_attributes$ ansible-doc -t module glu_update_device_attributesInstallation imlementation note:The 'pip install gluware-ansible-inventory' places the 'glu_devices.py' file in the '/usr/share/ansible/plugins/inventory/' directory for ansible to find the glu_devices inventory plugin.  The modules are placed in the '/usr/share/ansible/plugins/modules/'
directory for ansible to find the Gluware modules.The inventory plugin is just the 'glu_devices.py' file with no other files being used.
Package: glvi
glviglvi is a Python module for machine learning built on top of Scikit-learn and is distributed under the MIT license.glvi was developed by Mr. Li for evaluating variable importance heterogeneity through a global model built on a large time-space scope.glvi 0.1.4 was not supporting Python 2.7 and Python 3.4.glvi 0.1.4 and later require Python 3.5 or newer.glvi requires:Python (>= 3.5)NumPy (>= 1.11.0)SciPy (>= 0.17.0)Scikit-learn (>= 0.21.0)
User installationIf you already have a working installation of numpy, scipy, pandas and scikit-learn, the easiest way to install glvi is using ``pip``   ::
	
	pip install -U glvi

User guideCompute local variable importance based on decrease in node impurity ::from glvi import todi
r_t = todi.lovim(500, max_features=0.3, n_jobs=-1)
r_t.fit(train_x, train_y)
local_variable_importance = r_t.compute_feature_importance(X,Y,partition_feature = partition_feature, norm=True,n_jobs=-1)or compute local variable importance based on decrease in accuracy ::from glvi import meda
r_m = meda.lovim(500, max_features=0.3, n_jobs=-1)
r_m.fit(train_x, train_y_
local_variable_importance = r_m.compute_feature_importance(X,Y,partition_feature = partition_feature, norm=True,n_jobs=-1)
Package: glvrd
glvrd - неофициальный клиент к сервису glvrd.ruЭто неофициальный и не production-ready клиент для замечательного сервиса проверки текстов - glvrd.ru. Работает в обход API, так что на особую скорость советую не расчитывать.Устанавливается так:pipinstallglvrdПример кода:fromglvrdimportGlvrdClientclient=GlvrdClient()text='Это неофициальный и не production-ready клиент для замечательного сервиса проверки текстов - glvrd.ru. Работает в обход API, так что на особую скорость советую не расчитывать.'defprint_estimate(estimate,what_is_it):print(f'{what_is_it}:{estimate.estimate}/10')forerror_name,examplesinestimate.errors.items():print(f'{error_name}:')forexampleinexamples:print(f'\t{example}')print()print_estimate(client.estimate_clarity(text),'Чистота')print_estimate(client.estimate_readability(text),'Читаемость')... выдаст что-то вроде:Чистота: 8.1/10
Необъективная оценка:
	замечательного
Усилитель:
	особую

Читаемость: 8.8/10
В начале предложения нет глагола:
	Это неофициальный и не production-ready клиент для замечательного сервиса
Подозрение на парцелляцию:
	Работает в обход