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Project description

tkseem (تقسيم) is a tokenization library that encapsulates different approaches for tokenization and preprocessing of Arabic text.


Please visit readthedocs for the full documentation.


pip install tkseem



import tkseem as tk
tokenizer = tk.WordTokenizer()

tokenizer.tokenize("السلام عليكم")
tokenizer.encode("السلام عليكم")
tokenizer.decode([536, 829])


tokenizer.tokenize(open('data/raw/train.txt').read(), use_cache = True)

Save and Load

import tkseem as tk

tokenizer = tk.WordTokenizer()

# save the model

# load the model
tokenizer = tk.WordTokenizer()

Model Agnostic

import tkseem as tk
import time 
import seaborn as sns
import pandas as pd

def calc_time(fun):
    start_time = time.time()
    return time.time() - start_time

running_times = {}

running_times['Word'] = calc_time(tk.WordTokenizer)
running_times['SP'] = calc_time(tk.SentencePieceTokenizer)
running_times['Random'] = calc_time(tk.RandomTokenizer)
running_times['Disjoint'] = calc_time(tk.DisjointLetterTokenizer)
running_times['Char'] = calc_time(tk.CharacterTokenizer)


We show how to use tkseem to train some nlp models.

Name Description Notebook
Demo Explain the syntax of all tokenizers.
Sentiment Classification WordTokenizer for processing sentences and then train a classifier for sentiment classification.
Meter Classification CharacterTokenizer for meter classification using bidirectional GRUs.
Translation Seq-to-seq model with attention.

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