semvec/pypi-packages huggingface dataset:
ContinentInfo 1.2
Get the name of the continent in which a country is located in.

# ContinentInfo
----

![](https://forthebadge.com/images/badges/made-with-python.svg)


ContinentInfo is python package to get the name of the continent in which a country is located in.

### Installation

ContinentInfo requires minimum [Python 2.7](https://www.python.org/downloads/release/python-272/) to run.

```sh
$ pip install ContinentInfo
```
### Usage
```sh
$ from ContinentInfo import get_continent
$ country_name = get_continent('India')
$ country_name
$ 'Asia'
```

### License
[![MIT license](http://img.shields.io/badge/license-MIT-brightgreen.svg)](http://opensource.org/licenses/MIT)

**Copyright (c) 2019 Rohit Swami**

This project is licensed under the MIT License - see the LICENSE.txt file for details
jsonp 1.0.4
Simple pipeline json parser
Requires Python: >=3.7

# Simple pipeline json parser

It is as jq but better because you can use python syntax and you do not need to look in documentation every time.

## Install

```
pip install jsonp
```

## Usage

Pretty print
```
echo '{"userId":1,"id":1,"title":"delectus aut autem","completed":false}' | j
{
    "completed": false,
    "id": 1,
    "title": "delectus aut autem",
    "userId": 1
}
```

Modify data as you want until returned data is correct json object
```
echo '{"userId":1,"id":1,"title":"delectus aut autem","completed":false}' | j '{str(v):k for k, v in it.items()}'
{
    "1": "id",
    "False": "completed",
    "delectus aut autem": "title"
}

```
FastSent 0.2.0
FastSent is one of the solution for Sentiment classification using Recurrent Neural Networks(GRU)

FastSent is Sentiment Classification python library. It uses Sequential model for Sentiment classfication. FastSent is developed using GRU(Gated Recurrent Unit) model.

Requirement
------------

FastSent support Python 3.6 or newer.

Installation
------------

    pip install FastSent

Example
-------------

This package is being developed for sentiment classfication using Sequential model GRU(Gated Recurrent Unit).

	data = 'Sample.csv'
	labels = 'sentiment'
	text = 'content'

	f = FastSent()
	X_train, X_test, y_train, y_test = f.train_test_split(data, labels, text)
	trained_model = f.fit_train(X_train, y_train, 500, 50, 7789, 5, 4)
	prediction = f.predict(trained_model, X_test, y_train, 4)




where ``sample`` is a training file containing labels and text.

References
----------

DataSet Information
~~~~~~~~~~~~~~~~~~~

[1] Sample DataSet is being used for research purpose from  `*data.world* <https://data.world/crowdflower/sentiment-analysis-in-text>`.
seq2class 0.3.0
seq2class is developed for text classification using LSTM

seq2class is a one stop solution for text classification. Text classification is made easy via seq2class package using sequential model(LSTM). This package is build by harnesing the capabilities of  LSTM(Long short term Memory) model.

Requirement
------------

seq2class support Python 3.6 or newer.

Installation
------------


    pip install seq2class

Example
-------------

This package is being developed for text classification using sequential model.

	data = 'movies.csv'
	labels = 'title'
	text = 'genres'

	s = Sequence2class()
	X_train, X_test, y_train, y_test = s.train_test_split(data, labels, text)
	trained_model = s.fit_train(X_train, y_train, 500, 50, 7789, 5, 4)
	prediction = s.predict(trained_model, X_test, y_train, 4)


where ``movies.csv`` is a training file containing text and labels.

References
----------

DataSet information
~~~~~~~~~~~~~~~~~~~

[1]  movies.csv dataset have been used for research purpose from this `*link* <http://files.grouplens.org/datasets/movielens/ml-latest-small.zip>`.