semvec/pypi-packages huggingface dataset:
selector-standardization-beam 0.4.15
Data Standardization pipeline in Apache Beam for Selector project
Requires Python: >=3.7

# Selector-standardation-beam

Apache beam пайплайн стандартизации:
- читает данные протоколов (например, хранящиеся на GCS)
- вызывает selector-standardizers в виде масштабируемого пайплайна (исполняемого как локально, так и, например, на Dataflow, Spark и тп.)
- сохраняет результат стандартизации в виде Avro.

> GOOGLE_APPLICATION_CREDENTIALS=/Users/nzhiltsov/airflow-installation/google-configs/carbide-program-314404-b1f3be733966.json
selector-standardizers 0.8.3
Electoral Data Standardization classes for the Selector project
Requires Python: >=3.7

# Selector-standardizers

Модуль проекта Selector, реализующий основную логику обработки данных протоколов:
- распознавание уровня выборов
- распознавание уровня комиссии
- подбор схемы данных
- распознавание полей протокола
- конвертирование в стандартный формат, соответствующий схеме
- обучение ML-моделей распознавания.

Для работы тестов необходимо задать переменную окружения GOOGLE_APPLICATION_CREDENTIALS, указывающую путь к JSON файлу аутентификации Google Cloud Storage (GCS) проекта codeforrussia-selector.

> PYTHONPATH=src GOOGLE_APPLICATION_CREDENTIALS=<path_to_google_credentials_json> pytest

## Обучение модели распознавания полей протоколов

Обучение (точнее, fine-tuning) ML-модели осуществляется по текущей Avro схеме. Avro схема разрабатывается для выборов определенного уровня и типа. Pre-trained DeepPavlov RuBERT sentence encoder модель уже способна распознавать различные поля протоколов. Для улучшения ее качества используется fine-tuning модели по отношению к triplet loss на синтетических данных, сгенерированных из Avro схемы.

1) Генерируются синтетические данные для обучения из имен и алиасов полей протоколов в схеме.
2) Fine-tuning языковой модели по triplet loss.
3) Экспорт модели в формат, принимаемый Sentence-BERT.

Пример команды тренировки:
> PYTHONPATH=src python -m org.codeforrussia.selector.standardizer.recognizers.similarity.train --election-level REGIONAL --election-type PERSONAL --output-model-dir /tmp/codeforrussia-selector/ml-models/similarity-protocol-recognizer_1_0_0.zip
hypixelapitools 0.0.2
Provides useful tools for working with the Hypixel API.

# Hypixel API tools

The Hypixel API Tools library (HAT) is a library designed to make your work with the Hypixel API much easier.

## Installation

Run the following to install:

```
pip install hypixelapitools
```

## Usage

```python
import hypixelapitools as hat

player = hat.Player("API_KEY", "XYNIT")
r = player.request_api(hat.Api.PLAYER)

print(player.get_stat(r, hat.PlayerStats.PLAYER, "networkExp"))
```
jqed 0.1.4
Process JSON data interactively using JQ

# json interactive stream editor

A tool inspired by [Ultimate Plumber](https://github.com/akavel/up) that uses [jq](https://stedolan.github.io/jq) to process JSON and show you the results instantly, to enable greater interactivity.

[![asciicast](https://asciinema.org/a/313423.svg)](https://asciinema.org/a/313423)

[A manual for jq can be found here.](https://stedolan.github.io/jq/manual)

## Download & Install

```bash
sudo apt install jq  # <-- Install the jq tool, on MacOS install using `brew install jq`
pip3 install jqed
```

## Usage / Examples
```sh
cat some_file.json | jqed
ip --json link | jqed
```
mybrowse 0.5
MyBrowse is a simple browser written in Python3 using WebKit and GTK
Requires Python: >=3.6


# MyBrowse

MyBrowse is a simple webbrowser written in Python3 using GTK and WebKit.

[![Packaging status](https://repology.org/badge/vertical-allrepos/mybrowse.svg)](https://repology.org/project/mybrowse/versions)

## Support

 * #mybrowse on [irc.libera.chat](https://libera.chat) ([webchat](https://web.libera.chat/?channel=#mybrowse), [via Matrix](https://matrix.to/#/#mybrowse:libera.chat))

## Installation

### PyPI

The command

    pip install --user -U mybrowse

will install MyBrowse using pip inside your Home-Directory and can be used to upgrade it too.

### Debian/Ubuntu

Installation on Ubuntu based distributions is possible with a PPA:

    sudo apt-add-repository ppa:tuxifreund/mybrowse
    sudo apt-get install mybrowse

Additionally MyBrowse is present in the [MPR](https://mpr.hunterwittenborn.com/packages/mybrowse). Installation is pretty straightforward:

    git clone https://mpr.hunterwittenborn.com/mybrowse.git && cd mybrowse
    makedeb -s && sudo dpkg -i "$PWD"/mybrowse_*.deb

### Arch

MyBrowse is available via the AUR. To install it just type:

    git clone https://aur.archlinux.org/mybrowse.git && cd mybrowse
    makepkg -si

Or use your prefered AUR-helper. It would be a pleasure, if you’d vote for the AUR under [https://aur.archlinux.org/packages/mybrowse](https://aur.archlinux.org/packages/mybrowse).

## Features

 * loading websites ;)
 * bookmark pages
 * seperate search and addressbar
 * history
 * automatic use of https

## Donating

Donating is currently only possible via Bitcoin: bc1qfg6uy36es7ycsyw5g50wjz45hfs5ke5w8twfap
ARAMBHML 0.2.4
An Auto ML framework that solves Classification Tasks
Keywords: auto ml,python,classification problem,machine learning

# Auto ML for Solving Classification Problems for Tabular Data

[![forthebadge made-with-python](http://ForTheBadge.com/images/badges/made-with-python.svg)](https://www.python.org/)                 
[![Python 3.6](https://img.shields.io/badge/python-3.6-blue.svg)](https://www.python.org/downloads/release/python-360/)   


## Functionality of the Auto ML Framework

- Detects the type of problem from the given target feature
- Does EDA on it's own
- Finds out if there are null values present or not
- If null values are present they are treated specifically with the class they are present in
- Forms train-test split on it's own
- Applies 6 ML models to show the use which model performs the best

## Usage

- Make sure you have Python installed in your system.
- Run Following command in the CMD.
 ```
  pip install ARAMBHML
  ```
## Example

 
# test.py
 ```
  from ARAMBHML import arambhNet
  ```
 ```
  new = arambhNet(path,target) #target is the dependent feature and path is the path of csv file
  ```
 ```
  new.get_model_details(path,target)
  ```

## Run the Above Commands to get the results.
 

**NOTE** There are more than functionalities available, you can press "tab" after "new." to get the options

## Here you can perform EDA and also see how do models perform on the dataset.

## Note 
- I have tried to implement all the functionality, it might have some bugs also. Moreover a number of more functionalities would be added later.