semvec/pypi-packages huggingface dataset:
finglish 1.4.4
Finglish-to-Persian converter.
License: MIT
Keywords: persian,finglish

Finglish to Persian Converter
=============================

This library provides a Finglish-to-Persian convertor.

::

    >>> from finglish import f2p
    >>> f2p('man va to')
    [('آسمان آبی', 1.0), ('عثمان آبی', 0.32267613972764947), ('آسمان ابی', 0.2849966510381782), ('عثمان ابی', 0.09196161919230733), ('اسمان آبی', 0.008288928359976317), ('اسمان ابی', 0.002362316823288629), ('آسمان عبی', 0.0006697923643670462), ('عثمان عبی', 0.0002161260145530137), ('اسمان عبی', 5.5518609242976e-06)]

(Your browser might be showing some garbled text due to the presence of
Persian text. Try this in Python shell.)

The return value is a list of possibilities, each with a confidence
value in the [0.0, 1.0] range.
retrieveStack 0.0.1
retrieveStack
License: MIT

.. image:: https://travis-ci.org/MacHu-GWU/elementary_math-project.svg?branch=master

.. image:: https://img.shields.io/pypi/v/retrieveStack.svg

.. image:: https://img.shields.io/pypi/l/retrieveStack.svg

.. image:: https://img.shields.io/pypi/pyversions/retrieveStack.svg


Welcome to retrieveStack Documentation
===============================================================================
retrieveStack is an analytical framework by resembling a feedforward neural network and using stacked generalization in multiple levels to improve accuracy in classification (or regression) problems.
In contrast to conventional feedforward neural-network-like stacked structures, retrieveStack gathers the information of models in all layers, choose good models from multiple layers by comparing to output layer, and combine them with output layer model to optimize final prediction. Below is the pictorial description of how retriveStack working:


.. image:: https://raw.githubusercontent.com/Robin888/retrieveStack-project/master/desc.jpg

retrieveStack gives better prediction than the best single model contains in first layer. Please note its performance relies on a mix of strong and diverse single models in order to get the best out of this analytical framework


**Quick Links**
-------------------------------------------------------------------------------
- `GitHub Homepage <https://github.com/Robin888/retrieveStack-project>`_
- `Online Documentation <https://github.com/Robin888/retrieveStack-project/tree/master/documentation>`_
- `PyPI download <https://pypi.python.org/pypi/retrieveStack>`_
- `Install <install_>`_
- `Issue submit and feature request <https://github.com/Robin888/retrieveStack-project/issues>`_
- `API reference and source code <https://github.com/Robin888/retrieveStack-project/blob/master/documentation/Functions%20and%20Parameters.pdf>`_
- `Tutorial <https://github.com/Robin888/retrieveStack-project/blob/master/documentation/Tutorial.ipynb>`_


.. _install:

Install
-------------------------------------------------------------------------------

``retrieveStack`` is released on PyPI, so all you need is:

.. code-block:: console

        $ pip install retrieveStack

To upgrade to latest version:

.. code-block:: console

        $ pip install --upgrade retrieveStack