Saravanan666/pypi-org huggingface dataset:
Package: huggingface-tool
huggingface_toolTools for loading, upload, managing huggingface models and datasetsInstallationpip install huggingface-toolUsageFirstly, you need to login withhuggingface-cli login(you can create or find your token atsettings).Download and save a repo with:htool save-repo <repo_id> <save_dir> -r <model/dataset>.-rmeans the repo is a model or dataset repo. By default, it is a model repo.For example:htool save-repo OpenRL/tizero ./tizeroFor example:htool save-repo OpenRL/DeepFakeFace ./DeepFakeFace -r datasetDownload and save a file with:htool save-file <repo_id>:<remote_filepath> <save_dir> -r <model/dataset>.-rmeans the repo is a model or dataset repo. By default, it is a model repo.For example:htool save-file OpenRL/tizero:actor.pt ./tizeroFor example:htool save-file OpenRL/DeepFakeFace:README.md ./DeepFakeFace -r datasetDownload and save transformer models with:htool save-model <model_class> <model_name> <save_dir>For example:htool save-model AutoModelForCausalLM gpt2 ./gpt2Download and save tokenizer with:htool save-tk <tokenizer_name> <save_dir>For example:htool save-tk gpt2 ./gpt2Download and save dataset with:htool save-data <dataset_name> <save_dir>For example:htool save-data daily_dialog ./daily_dialogDownload and save diffusion models with:htool save-dm <model_name> <save_dir>For example:htool save-dm google/ddpm-cat-256 ./google/You can also use htool to upload datasets and models to huggingface.Upload a file with:htool upload-file <local_filepath> <organization_or_username/repo_name>:<remote_filepath> -r <model/dataset>.-rmeans the repo is a model or dataset repo. By default, it is a model repo.For example:htool upload-file README.md OpenRL/tizero:README.mdFor example:htool upload-file README.md OpenRL/DeepFakeFace:README.md -r datasetUpload a directory with:htool upload-dir <local_dirpath> <organization_or_username/repo_name>:<remote_dirpath> -r <model/dataset>.-rmeans the repo is a model or dataset repo. By default, it is a model repo.For example:htool upload-dir ./tizero OpenRL/tizero:./For example:htool upload-dir ./tizero/models OpenRL/tizero:./modelsFor example:htool upload-dir ./DeepFakeFace OpenRL/DeepFakeFace:./ -r datasetFor example:htool upload-dir ./DeepFakeFace/images OpenRL/DeepFakeFace:./images -r datasetUpload dataset with:htool upload-data <local_dataset_dir> <organization_or_username/dataset_name>For example:htool upload-data ./daily_dialog OpenRL/daily_dialogUpload model with:htool upload-model <local_model_dir> <organization_or_username/model_name>For example:htool upload-model ./tizero OpenRL/tizeroCiting huggingface_toolIf our work has been helpful to you, please feel free to cite us:@misc{huggingface_tool2023,
    title={huggingface_tool},
    author={OpenRL Contributors},
    publisher ={GitHub},
    howpublished ={\url{https://github.com/OpenRL-Lab/huggingface_tool}},
    year={2023},}
Package: hugging-gan-test
Geniverse libraryThe Geniverse Project. Created by Victor Perez (@viccpoes) & Javier Ideami (@ideami).
Welcome to Geniverse. Build and train a universe of state of the art generative models and use powerful tools to generate and create.Geniverse Alpha is the first creative platform we are building. It is a tool that allows you to generate visual assets using generative A.I. models through a minimalistic and powerful prompt engineering interface.With Geniverse Alpha you can:Guide the generative A.I. architectures by creating prompts within a flexible canvas, using texts and images.Generate results using different cutting edge generate A.I. architectures, being ab le to tweak the key settings of those models.Create videos of interpolations between a set of results, and create videos of zoom processes beginning at one of the results.Download the images and videos generated by the A.I.Access the tool from any device. Geniverse Alpha is optimized for the Chrome browser on PC and Android, and the Safari browser on iOS Mac and iPhone.Forks are integrated into this repo usingsubmodules. To properly load the forks, the following command must be executed:gitsubmoduleinit
Package: hugginglens
VisionTransformerLensThis is an extension ofTransformerLensthat integrates other modalities usingHuggingFace. It incorporates components of TransformerLens such asHookPointsand retains signature functionalities likerun_with_cache(), to make it straightforward for users to explore multimodal mechanistic interpretability.Installationgit clone git@github.com:jannik-brinkmann/vision-transformerlens.gitExamplefrom PIL import Image
import requests
from transformers import CLIPProcessor

from lens import HookedVisionTransformer

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# load a model (e.g. CLIP)
model_name_or_path = "openai/clip-vit-base-patch32"
model = HookedVisionTransformer.from_pretrained(model_name_or_path, device)
processor = CLIPProcessor.from_pretrained(model_name_or_path)

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

# extract image features
inputs = processor(text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True)
inputs.to(device)

# run the model and get outputs and activations
outputs, activations = model.run_with_cache(**inputs)Citation@misc{brinkmann2023visiontransformerlens
  title   = {VisionTransformerLens},
  author  = {Brinkmann, Jannik},
  journal = {https://github.com/jannik-brinkmann/VisionTransformerLens},
  year    = {2023}
}
Package: hugging-quik
hugging-quikA helper package to make loading/unloading huggingface models quik-erThis was originally part ofpytorch-quik, but its requirements are large, and hugging-quik is a simple way to pull and use huggingface.co pretrained models and tokenizers.hugging-quik does the following:
- get_tokenizer: pull a tokenizer from the huggingface suite of models
- save_tokenizer: save the tokenizer to disk
- get_encodings: use the tokenizer to get text encodings
- get_pretrained_model: pull a model from the huggingface suite
- save_pretrained_model: save the model to disk
Package: hugin
HuginHugin helps scientists run Machine Learning experiments on geospatial raster data.Overall Hugin aims to facilitate experimentation with multiple machine learning problems, like:ClassificationSegmentationSuper-ResolutionCurrently Hugin builds on top of the Keras machine learning library but it also aims to support, in the future, additional backends like scikit-learn.Documentation for Hugin is available athttps://hugin-eo.readthedocs.io/AcknowledgmentsThis project is supported by the European Space Agency through the ML4EO project.
Package: huginn
huginnNamed for one of the Norse god Odin's ravens who would gather information for him. A package to facilitate the investigation of anomalous spikes in public interest in an entity, then fetch and summarize news stories relevant to those spikes.Installation and SetupRequires Python=3.6huginnisavailable on PyPiand installable via pip:pip install huginnSpacy modelsDownload English spacy models by running the following in the command line:$ python3 -m spacy download enNYT API KeyThis project requires the usage of the New York Times article search API.  Request a key atdeveloper.nytimes.com. You'll need to set your NYT API key as a system environment variable. Open the file~/.bashrc(or~/.bash_profileif the.bashrcdoesn't exist) in any Unix based system (Mac, Ubuntu, etc), and add the following two lines to the end of the file:# Setting NYT API Key as an environment variable
export NYT_API_KEY="YOUR_KEY_HERE"WhereYOUR_KEY_HEREis your NYT API key, enclosed in quotes.This  is necessary for requesting relevant links for a potential entity's anomalies.Usagehuginn is based around theHuginnobject. A user only need provide the entity that they are interested in.fromhuginnimportHuginnpoint72=Huginn('Point72')At this point huginn fetches Google Trends search interest on this entity looking back to 2003.  We can then find the most anomalous dates and plot the Google trends search interest with the anomalies:point72.get_anomalies()point72.plot_interest_with_anomalies()We then fetch news articles and relevant information related to our anomalies:point72.get_articles_info()Now we can get summaries, either specific to each anomaly or globally:point72.get_global_summary()
point72.get_articles_info_and_summary_after_LDA()These require downloading some rather large summarization models to run.
Package: hugit
HugitWarning: this code is very much a work in progress and is primarily being intended for a particular workflow. It may not work well (or at all) for your workflow.hugitis a command line tool for loading ImageFolder style datasets into a 🤗datasetsDatasetand pushing to the 🤗 hub.The primary goal ofhugitis to help quickly get a local dataset into a format that can be used for training computer vision models.hugitwas developed to support the workflow forflyswotwhere we wanted a quicker iteration between creating new training data, training a model, and using the new model insideflyswot.Supported formatsAt the momenthugitsupports ImageFolder style datasets i.e:data/dog/dog1.jpgcat/cat.1.jpgFeaturesA command line interface for quickly loading a dataset stored on disk into a 🤗datasets.DatasetPush your local dataset to the 🤗 hubGet statistics about your dataset. These statistics focus on 'high level' statistic that would be useful to include in Datasheets and Model Cards. Currently these statistics include:label frequencies, organised by splittrain, test, valid split sizesInstallationYou can installHugitviapipfromPyPI, inside a virtual environment installhugitusing$pipinstallhugitAlternatively, you can usepipxto installhugit$pipxinstallhugitUsageYou can see help forhugitusinghugit --helpUsage: hugit [OPTIONS] COMMAND [ARGS]...

  Hugit Command Line

Options:
  --help  Show this message and exit.

Commands:
  convert_images      Convert images in directory to `save_format`
  push_image_dataset  Load an ImageFolder style dataset.To load an ImageFolder style dataset onto the 🤗 Hub you can use thepush_image_datasetcommand.Usage: hugit push_image_dataset [OPTIONS] DIRECTORY

  Load an ImageFolder style dataset.

Options:
  --repo-id TEXT                  Repo id for the Hugging Face Hub  [required]
  --private / --no-private        Whether to keep dataset private on the Hub
                                  [default: private]
  --do-resize / --no-do-resize    Whether to resize images before upload
                                  [default: do-resize]
  --size INTEGER                  Size to resize image. This will be used on the
                                  shortest side of the image i.e. the aspect
                                  rato will be maintained  [default: 224]
  --preserve-file-path / --no-preserve-file-path
                                  preserve_orginal_file_path  [default:
                                  preserve-file-path]
  --help                          Show this message and exit.Under the hoodhugitusestyped-settings, which means that configuration can either be done through the command line or through aTOMLfile. Seeusagefor more detailed discussion of how to usehugit.ContributingIt is likely thatHugitmay only work for our particular workflow. With that said if you have suggestions please open an issue.LicenseDistributed under the terms of theMIT license,Hugitis free and open source software.IssuesIf you encounter any problems,
pleasefile an issuealong with a detailed description.CreditsThis project was generated from@cjolowicz'sHypermodern Python Cookiecuttertemplate.
Package: hug-middleware-cors
# hug CORS middlewareA [hug](https://github.com/timothycrosley/hug) middleware for allowing CORS (cross-origin resource sharing) requests from hug servers.## Installation```bashpip install hug_middleware_cors```## Usage```pythonimport hugfrom hug_middleware_cors import CORSMiddlewareapi = hug.API(__name__)api.http.add_middleware(CORSMiddleware(api))@hug.post('/demo')def demo(name: 'your name'):return {"result": "Hello {0}".format(name)}```## Demoing1. Check out this repository and navigate to its root2. Start a hug server on port 8000 using: `hug -f server.py`3. Start a static http server on port 8080 using `python -m http.server 8080`4. Go to [http://localhost:8080](http://localhost:8080) to send a preflighted CORS request to the hug server.## LicenseMIT LicenseSee LICENSE for details
Package: hugo-deployer-git
hugo-deployer-gitDeploy your hugo site using one commandInspired byhexo-deployer-git.Installationpip3 install hugo-deployer-gitSetupIn yourconfig.toml, insert this section with your configs.[hugo-deployer-git]# requiredrepo="your github page repo"branch="master"# optoinalname="haha"email="test@testtest.com"message=""build-config="--minify"How to usecd to your hugo project and usehugo-deployer-git.
Package: hugo_jupyter
PublishJupyternotebooks withHugo.Documentation:https://knowsuchagency.github.io/hugo_jupyterSource:https://github.com/knowsuchagency/hugo_jupyterInstallationpipinstallhugo_jupyterUsagecdroot_of_hugo_projecthugo_jupyter--initThis will create anotebooksdirectory at the root of your hugo project if it doesn’t yet exist.
Any jupyter notebooks you want rendered should go in thenotebooksdirectory.In addition, a fabfile.py script will be written at the project root.fabserveAutomatically initializes your jupyter server, hugo server, and watchdog to re-render
your jupyter notebooks to markdown for hugo as you create and edit them.Jupyter NotebooksAny notebooks that you create will needfront matterfor hugo to know how to render the content.Once you edit the name of the jupyter notebook to something other thanUntitled*.ipynb, hugo-jupyter will
automatically edit the notebook’s metadata to enable rendering with jupyter. You may need to reload the
notebook page to see the changes in the metadata.There will also be ahugo-jupyterdictionary in the notebook’s metadata with arender-tofield
automatically set tocontent/post/. You can edit this field to edit where the notebook’s markdown
will be rendered to.
Package: hugpee
HugPeeHugPee simplifies the creation of RESTful CRUD endpoints in hug, for peewee models.Installing HugPeeInstalling Hugpee is very simple, just run:pip3installhugpee--upgradeUsageConsider a scenario wherePersonis our peewee model. Creating the CRUD endpoints for hug is very simple with HugPee:hugpee.HugPee(Person)And then load them to hug.@hug.extend_api()defget_all_hugs():return[modelendpoint]Here is the full working example:importhugfrompeeweeimportSqliteDatabase,ModelfrompeeweeimportPrimaryKeyField,CharField,DateField,BooleanFieldimporthugpeedb=SqliteDatabase(":memory:")classPerson(Model):ID=PrimaryKeyField()name=CharField()birthday=DateField()is_relative=BooleanField()classMeta:database=dbdb.create_tables([Person])hugpee.HugPee(Person)@hug.extend_api()defget_all_hugs():return[hugpee]This will create the following endpoints:HTTP ACTIONURI(s)Accepted ParametersPOST/personname, birthday, is_relativeGET/person , /person/{id}idPUT/person , /person/{id}id, name, birthday, is_relativeDELETE/person , /person/{id}idNote that the primary key, in this case the id, can be provided either as part of the URI or as a parameter.
Package: hug_peewee
hug\_peewee===========|PyPI version| |Build Status| |Coverage Status| |License| |Join the chatat https://gitter.im/timothycrosley/hug\_peewee|An extension to hug that adds clean integration with the peewee ORM:.. code:: pyimport hug_peeweedb = hug_peewee.connection.manage(__name__, 'sqlite', 'my.db')Works with hug 2.1.0 or greater.Installing hug\_peewee======================Installing hug\_peewee is as simple as:.. code:: bashpip3 install hug_peewee --upgradeIdeally, within a virtual environment.What is hug\_peewee?====================An extension to hug that adds clean integration with the peewee ORM--------------Thanks and I hope you find hug\_peewee helpful!~Timothy Crosley.. |PyPI version| image:: https://badge.fury.io/py/hug_peewee.svg:target: http://badge.fury.io/py/hug_peewee.. |Build Status| image:: https://travis-ci.org/timothycrosley/hug_peewee.svg?branch=master:target: https://travis-ci.org/timothycrosley/hug_peewee.. |Coverage Status| image:: https://coveralls.io/repos/timothycrosley/hug_peewee/badge.svg?branch=master&service=github:target: https://coveralls.io/github/timothycrosley/hug_peewee?branch=master.. |License| image:: https://img.shields.io/github/license/mashape/apistatus.svg:target: https://pypi.python.org/pypi/hug_peewee/.. |Join the chat at https://gitter.im/timothycrosley/hug\_peewee| image:: https://badges.gitter.im/Join%20Chat.svg:target: https://gitter.im/timothycrosley/hug_peewee?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge
Package: hug_yaml
hug_yamlAn extension for hug that provides YAML input formats, output formats,
and documentation:importhugimporthug_yamlhug.API(__name__).extend(hug_yaml.input_format)@hug.not_found(output=hug_yaml.output)defdocumentation(hug_api,hug_api_version):returnhug_yaml.not_found.documentation(hug_api,hug_api_version)Installing hug_yamlInstalling hug_yaml is as simple as:pip3installhug_yaml--upgradeIdeally, within a virtual environment.Why hug_yaml?An extension for hug that provides YAML input formats, output formats,
and documentation.Thanks and I hope you find hug_yaml helpful!~Timothy Crosley
Package: huhu
huhu===================## * This Project Under Development *### Installationpip install huhu### If you don't have pip:easy_install pip### Syntax```sh* setdeğişken = bu bir değişken* listliste = bu, bir, liste* writeyaz(değişken)* loopingyaz(kelime dön kelimeler)* rangesayilar = 1..8yaz(sayi) dön sayilarharfler = a..zyaz(harf) dön harfler```if you have any clean syntax idea, please create an issue for discussion.
Package: huik-module
huik-moduleHow to install:pip install huik-moduleCommand Line Interface UsageCommand Line Interface are built withClick.Usecommand --helpfor built in help info.tool list moduletlTool list (tl) provide a way to make user defined shortcuts for frequently used shell commands.tlto start tool list.tl --helpto show help menu.tl [command key]to quickly invoke saved shell command.Hint: Shell commands are ran in a new process, if a command is changing
shell behavior this change will not be reflected in the current shell.
If user want to retain such behavior, one way to useexec shellorexec zshat the end of command. (see folder module for example)folder modulefdFolder (fd) can mark folder as favorites and let user quickly navigate to that folder
in terminal.fd [favorite folder shortcut]cd to saved folder.fd -cconfig use bash or zsh as default shell.Under the hood,fduses python subprocess to run shell script.subprocess.run("cd /path/to/folder;exec zsh",shell=True,)The subprocess start another process and run shell command.
Withoutexec zsh, after the subprocess exits, the current shell
will remain in the same folder.exec zshorexec bashstart a shell in
the subprocess and allow user input in that process.OK moduleokOK module let user write / install plugins to extend its function.OK module also manages to import/export/sync configuration and plugins.Some included plugins are: Dictionarydef; calculatorcal; github toolsgitOne would need to use plugins if simple shell command is not sufficient
in certain complex using scenarios. For example, to handle user
inputs or pull data from the web.okorok --helpto show help menu.ok syncto configure Github gist id and user token for upload settings to github.
User token is not needed for download public gist settings but required for create/upload settigns.ok [command]to run plugin commands.ok plugins --helpto show help menu for configure plugins.ok plugins -sfor--show-foldershow plugins folder.PluginsPlugins are single python files and stored undercli/pluginsfolder. These individual files are read and loaded at runtime.Basic structure:importclick# other imports if needed@click.command()# optionally use click.group() to allow multicommands.@click.option('--option')# add other options or arguments if needed.defcli(option):"""Documents"""# handle option
Package: huiqanghui-mllib
Titlesummaryhuiqanghui_mllibhuqianghui machine learning exported libInstallpip install huiqanghui_mllibHow to useshowNNResult()showNNResult()iteration 0 loss 1.0987398093279945
iteration 100 loss 0.7129422012608996
iteration 200 loss 0.6887726930266873
iteration 300 loss 0.5436778425377103
iteration 400 loss 0.3427991213694607
iteration 500 loss 0.2921075022407062
iteration 600 loss 0.2590305040238777
iteration 700 loss 0.18236065020429468
iteration 800 loss 0.20049390033740888
iteration 900 loss 0.1258016234147451
iteration 1000 loss 0.1077102236889968
iteration 1100 loss 0.09524374700271392
iteration 1200 loss 0.08570292680104846
iteration 1300 loss 0.07833028635138949
iteration 1400 loss 0.07244152243591633
iteration 1500 loss 0.06753888969454228
iteration 1600 loss 0.06342742576765745
iteration 1700 loss 0.05992692038834323
iteration 1800 loss 0.05688405519515976
iteration 1900 loss 0.05419281310098801
iteration 2000 loss 0.05183075921618167
iteration 2100 loss 0.04966551995852915
iteration 2200 loss 0.04772533072984744
iteration 2300 loss 0.045992529755421833
iteration 2400 loss 0.04443767350873418
iteration 2500 loss 0.04300507373620072
iteration 2600 loss 0.04169778815729772
iteration 2700 loss 0.040501514675789135
iteration 2800 loss 0.03940982830968154
iteration 2900 loss 0.03840483967389475
training accuracy: 0.9933333333333333
Package: huisbaasje-client
Huisbaasje clientClient to communicate with the API behindHuisbaasje.Huisbaasje is an app and measuring device to monitor the usage of electricity and gas in real time.UsageSee theexampleon how to use this library.DevelopmentThis project usespipenvfor dependency and environment management.Install dependencies usingpipenv install --devTestingRun all tests usingpipenv run pytestPackagingCreate a package usingpython3 setup.py sdist bdist_wheelThis creates a package indistUpload the package usingpython3 -m twine upload dist/*
Package: huishi
绘诗诗词自动生成系统简介这里是绘诗诗词自动生成系统所提供的python库。您可以借用此库里面的如下函数，完成我们提供的诗词自动生成demo首先，您可以使用以下命令，在您的计算机中使用pip安装我们所提供的基于绘诗诗词自动生成系统设计的python库。pip install huishi如果您想要了解代码中模型的运行原理，或者希望借用我们的预训练模型训练自己的NLP模型，可以参考我们的Github仓库或者gitee仓库在这个python库中，我们为您提供了以下API：key_to_poem_V(keywords) ;关键词到五言诗
key_to_poem_VII(keywords);关键词到七言诗
seq_to_poem_V(keywords); 描述语句到五言诗
seq_to_poem_VII(keywords);描述语句到七言诗其分别代表输入关键字或者一段对诗句的描述语言，利用模型自动输出五言或者七言诗句作为返回值。输入参数和返回值都是序列类型。如果在使用中有任何问题，可以通过我们的邮箱或者直接在项目负责人中联系我们，我们会给予力所能及的最大帮助。如果觉得我们的项目对您有帮助的话，请麻烦去我们的项目中为我们点个⭐，谢谢了！
Package: huispedia
huispediaPython library to get information about houses fromhuispedia.nl.DISCLAIMER: this is not an official API and doesn't use one. The script is provided as is (see the LICENSE) and for personal usage only. Please, contact the huispedia team (Huispedia BV) if you need a stable API for extensive or commercial usage.Installationpython3-mpipinstallhuispediaUsageimporthuispediahouses=huispedia.get_houses(price_max=295000,con_year_min=1970)print(houses[0])Output example (anonymized, just in case):{'id':'...','street':'Examplestraat','street_slug':'examplestraat','hnum':'13','hchar':'A','city_name':'Example','city_slug':'example','postcode':'1234AB','woonoppervlakte':'13','perceeloppervlakte':'13','aantal_kamers':'4','bouwjaar':'1984','object_type':'woonhuis','img_src':'example.jpg','status':'TE KOOP','status_updated_at':'12345678','status_updated_by':'REALTOR','price_sale':'265000','price_rent':'0','price_search':'265000','price_min_bid':'0','price_aot_indication':'0','price_m2':'1234','price_valuation':'265000','lat':'52.485558','lon':'4.658634','user_id':'0','user_firstname':'','office_name':'Example Makelaar','user_picture':'','office_id':'123','office_logo':'id_123.png','show_user_info':'1','available_on_term_at':'0','open_house_date':'0','counts_images':'42','counts_images360':'0','counts_floorplans':'3','counts_videos':'0','inside':'1',}