semvec/pypi-packages huggingface dataset:
tensorflow-datasets-ko 0.2.3
Custom TensorFlow Datasets for Korean Text Data
Requires Python: >=3.6
Keywords: TensorFlow,Dataset,C4,Korean,Python

# tensorflow-datasets-ko

Custom TensorFlow Datasets for Korean Text Data

![Hits](https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=tensorflow-datasets-ko)

# Requirements

```
tensorflow-datasets[c4]
```

# Installation

```bash
pip install tensorflow-datasets-ko
```

# Reference

- https://www.tensorflow.org/datasets
- https://www.tensorflow.org/datasets/catalog/c4
- https://www.tensorflow.org/datasets/add_dataset
- https://github.com/tensorflow/datasets
- https://github.com/google-research/text-to-text-transfer-transformer#c4
vtt2text 0.1.3
A small package to clean up the content of a subtitle file to text
Requires Python: >=2.7

# vtt2text

[![vtt2text](https://img.shields.io/badge/repo-vtt2text-brightgreen)](https://github.com/vuanhtuan1012/vtt2text)
![Python](https://img.shields.io/badge/Python-3.6-blue.svg)
![MIT](https://img.shields.io/badge/license-MIT-important.svg)
![Size](https://img.shields.io/github/repo-size/vuanhtuan1012/vtt2text.svg)
![Contributors](https://img.shields.io/github/contributors/vuanhtuan1012/vtt2text.svg)

## Install

```
pip install vtt2text
```

## Usage

- `vtt2text.clean(filepath)`: return a clean text containing content of `vtt` file input.
- `vtt2text.to_file(filepath)`: save clean content to a text file. By default, the output file has extension `.txt` and the same name with the input file.

## Example

Click [here](https://github.com/vuanhtuan1012/vtt2text/blob/main/test.py) to see an example.
robotframework-netaddr 0.0.4
Robotframework keyword for the python netaddr library
Requires Python: >=3.5
Keywords: netaddr,robotframework,ip address,ipv4,ipv6,mac address,network

# robotframework-netaddr

Provides a class which wraps much of the netaddr (https://netaddr.readthedocs.io/en/latest/introduction.html) library functionality.

The wrapped functions are accesible within Robot Framework and can be used there as keywords.
jsonut 0.1.0
JSON Dataclass Object Mapper
Requires Python: >=3.6

#JSONUT
*jsonut* can be used to convert json objects to Python data class objects and vice versa.

## How to Use
### Example
``` python
from dataclasses import dataclass, field
from typing import *

import jsonut

# Sample Dataclass Object
@dataclass
class SampleObject(jsonut.JsonSerializable):
    name: str = field(default_factory=str)
    numbers: List[int] = field(default_factory=list)

    def __eq__(self, other):
        if not isinstance(other, self.__class__):
            return False

        if self.name != other.name:
            return False

        if self.numbers != other.numbers:
            return False

        return True


# Create an object
# sample_obj = create_sample_object()

# Serialization
serialized = serialize(sample_obj)

# Deserialization
deserialized = deserialize(serialized)
```

## How to Deploy to PyPI
refer with: https://packaging.python.org/tutorials/packaging-projects/