tnkeeh (تنقيح) is an Arabic preprocessing library for python. It was designed using re for creating quick replacement expressions for several examples.

Installation

pip install tnkeeh

Features

• Quick cleaning
• Segmentation
• Normalization
• Data splitting

Examples

Data Cleaning

import tnkeeh as tn
tn.clean_data(file_path = 'data.txt', save_path = 'cleaned_data.txt',)

Arguments

• segment uses farasa for segmentation.
• remove_diacritics removes all diacritics.
• remove_special_chars removes all sepcial chars.
• remove_english removes english alphabets and digits.
• normalize match digits that have the same writing but different encodings.
• remove_tatweel tatweel character ـ is used a lot in arabic writing.
• remove_repeated_chars remove characters that appear three times in sequence.
• remove_html_elements remove html elements in the form with their attirbutes.
• remove_links remove links.
• remove_twitter_meta remove twitter mentions, links and hashtags.
• remove_long_words remove words longer than 15 chars.
• by_chunk read files by chunks with size chunk_size.

HuggingFace datasets

import tnkeeh as tn 
from datasets import load_dataset

dataset = load_dataset('metrec')

cleaner = tn.Tnkeeh(remove_diacritics = True)
cleaned_dataset = cleaner.clean_hf_dataset(dataset, 'text')

Data Splitting

Splits raw data into training and testing using the split_ratio

import tnkeeh as tn
tn.split_raw_data(data_path, split_ratio = 0.8)

Splits data and labels into training and testing using the split_ratio

import tnkeeh as tn
tn.split_classification_data(data_path, lbls_path, split_ratio = 0.8)

Splits input and target data with ration split_ratio. Commonly used for translation

tn.split_parallel_data('ar_data.txt','en_data.txt')

Data Reading

Read split data, depending if it was raw or classification

import tnkeeh as tn
train_data, test_data = tn.read_data(mode = 0)

Arguments

• mode = 0 read raw data.
• mode = 1 read labeled data.
• mode = 2 read parallel data.

Contribution

This is an open source project where we encourage contributions from the community.

License

MIT license.

Citation

@misc{tnkeeh2020,
  author = {Zaid Alyafeai and Maged Saeed},
  title = {tkseem: A Preprocessing Library for Arabic.},
  year = {2020},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/ARBML/tnkeeh}}
}