semvec/pypi-packages huggingface dataset:
ttd-cli-gpu 0.0.3
Transliteration of Tunisian Dialect from arabizi to Tunisia Arabic
Requires Python: >=3.6

# TTD CLI

TTD CLI command line interface of Transliteration Tunisian arabic Dialect (TTD) with LSTM and GRU based model.

## installation 

install with pip

`pip install ttd_cli-gpu`

install from source

`git clone https://github.com/suai-tn/ttd_cli-gpu.git`

or if you have password-protected SSH key

`git clone git@github.com:suai-tn/ttd_cli-gpu.git`

go to the ttd_cli directory

`cd ttd_cli-gpu`

install with pip

`pip install .`

## usage

`ttd --model_type=[gru | lstm ] --doc=[path_to_doc] --out=[path_out_file] --f=[0] --t=[100] --gpu=True --escape key,value ə,a œ,oe`

![](example.gif)


`ttd --txt ya aymen chbik lyouma `


![](example1.png)
ttd-cli 0.0.3
Transliteration of Tunisian Dialect from arabizi to Tunisia Arabic
Requires Python: >=3.6

# TTD CLI

TTD CLI command line interface of Transliteration Tunisian arabic Dialect (TTD) with LSTM and GRU based model.

## installation 

install with pip

`pip install ttd_cli`

install from source

`git clone https://github.com/suai-tn/ttd_cli.git`

or if you have password-protected SSH key

`git clone git@github.com:suai-tn/ttd_cli.git`

go to the ttd_cli directory

`cd ttd_cli`

install with pip

`pip install .`

## usage

`ttd --model_type=[gru | lstm ] --doc=[path_to_doc] --out=[path_out_file] --f=[0] --t=[100] --gpu=True --escape key,value ə,a œ,oe`

![](example.gif)

`ttd --txt ya aymen chbik lyouma `


![](example1.png)
pyevals102 1.0
A simple Python Package for Model Evalutaion

<!-- PROJECT SHIELDS -->
<!--
*** I'm using markdown "reference style" links for readability.
*** Reference links are enclosed in brackets [ ] instead of parentheses ( ).
*** See the bottom of this document for the declaration of the reference variables
*** for contributors-url, forks-url, etc. This is an optional, concise syntax you may use.
*** https://www.markdownguide.org/basic-syntax/#reference-style-links
-->


<!-- TABLE OF CONTENTS -->
## Table of Contents

* [About the Project](#about-the-project)
* [Built With](#built-with)
* [Installation](#installation)
* [Usage](#usage)
* [Future Work](#Futurework)
* [Contact](#contact)


<!-- ABOUT THE PROJECT -->
## About The Project

A very elegant and simple library to evaluate models.

This module will evaulate the Classification problems and Regression problems with 13 and 7 algorithms respectively. 

The Classification algorithms are KNN,LogisticRegression,DecisionTreeClassifier, RandomForestClassifier, SupportVectorClassifier, QuadraticDiscriminantSnalysis, SGDClassifier, XgBoost,AdaBoost, CalibratedClassifier, MultinomialNB, BernoulliNB, GaussianNB.

The Regression algorithms are LinearRegression, PolynomialRegression, RidgeRegression, LassoRegression, SupportVectorRegressor, GradientBoostingRegression, PLSRegression.

We also have implmented the Adjusted R Squared method as the Regression Metric Evaluation.


### Built With

This project is built mainly with the help sklearn module and is written in Python.


### Installation


1. Clone the repo
```sh
git clone https://github.com/https://github.com/Anand-gokul/Pyevals/.git
```

2. Install using pip or pip3
```commandline
pip3 install pyevals==1.0

(or)

pip install pyevals

```


<!-- USAGE EXAMPLES -->
## Usage


```python
import pyevals

# For Classification

Object = pyevals.build(x_train,x_test,y_train,y_test,'classification')
Object.evaluate()

# For Regression

Object = pyevals.build(x_train,x_test,y_train,y_test,'regression')
Object.evaluate()

  
```

### Future Work

In this version we are only providing the reports, but soon we will be adding plots too.We will also try to implement hyperparameter optimization techniques to get the better results. We will also try to implement other algorithms in classification and regression soon. 

<!-- CONTACT -->
## Contact


Sai Gokul Krishna Reddy Talla - [@Krish](https://www.linkedin.com/in/gokul-talla) - gokulkrishna.talla@gmail.com

Ananda Datta Sai Phanindra Tangirala - [@Anand](https://www.linkedin.com/in/ananda-datta-sai-phanindra-tangirala-62a4b5185) - tangiralaphanindra@gmail.com

Anirudh Palaparthi - [@anirudh8889](https://twitter.com/anirudh8889) - aniruddhapnbb@gmail.com

Project Link: [https://github.com/Anand-Gokul/Pyevals](https://github.com/Anand-gokul/pyevals101)
pyevals101 1.0
A simple Python Package for Model Evalutaion

<!-- PROJECT SHIELDS -->
<!--
*** I'm using markdown "reference style" links for readability.
*** Reference links are enclosed in brackets [ ] instead of parentheses ( ).
*** See the bottom of this document for the declaration of the reference variables
*** for contributors-url, forks-url, etc. This is an optional, concise syntax you may use.
*** https://www.markdownguide.org/basic-syntax/#reference-style-links
-->


<!-- TABLE OF CONTENTS -->
## Table of Contents

* [About the Project](#about-the-project)
* [Built With](#built-with)
* [Installation](#installation)
* [Usage](#usage)
* [Future Work](#Futurework)
* [Contact](#contact)


<!-- ABOUT THE PROJECT -->
## About The Project

A very elegant and simple library to evaluate models.

This module will evaulate the Classification problems and Regression problems with 13 and 7 algorithms respectively. 

The Classification algorithms are KNN,LogisticRegression,DecisionTreeClassifier, RandomForestClassifier, SupportVectorClassifier, QuadraticDiscriminantSnalysis, SGDClassifier, XgBoost,AdaBoost, CalibratedClassifier, MultinomialNB, BernoulliNB, GaussianNB.

The Regression algorithms are LinearRegression, PolynomialRegression, RidgeRegression, LassoRegression, SupportVectorRegressor, GradientBoostingRegression, PLSRegression.

We also have implmented the Adjusted R Squared method as the Regression Metric Evaluation.


### Built With

This project is built mainly with the help sklearn module and is written in Python.


### Installation


1. Clone the repo
```sh
git clone https://github.com/https://github.com/Anand-gokul/Pyevals/.git
```

2. Install using pip or pip3
```commandline
pip3 install Pyevals==1.0

(or)

pip install pyevals

```


<!-- USAGE EXAMPLES -->
## Usage


```python
import pyevals

# For Classification

Object = pyevals.build(x_train,x_test,y_train,y_test,'classification')
Object.evaluate()

# For Regression

Object = pyevals.build(x_train,x_test,y_train,y_test,'regression')
Object.evaluate()

  
```

### Future Work

In this version we are only providing the reports, but soon we will be adding plots too.We will also try to implement hyperparameter optimization techniques to get the better results. We will also try to implement other algorithms in classification and regression soon. 

<!-- CONTACT -->
## Contact


Sai Gokul Krishna Reddy Talla - [@Krish](https://www.linkedin.com/in/gokul-talla) - gokulkrishna.talla@gmail.com

Ananda Datta Sai Phanindra Tangirala - [@Anand](https://www.linkedin.com/in/ananda-datta-sai-phanindra-tangirala-62a4b5185) - tangiralaphanindra@gmail.com

Anirudh Palaparthi - [@anirudh8889](https://twitter.com/anirudh8889) - aniruddhapnbb@gmail.com

Project Link: [https://github.com/Anand-Gokul/Pyevals](https://github.com/Anand-Gokul/Pyevals)