Python package for Turkish Language.

Last update: Oct 09, 2022

Overview

PyTurkce

Python package for Turkish Language.

Documentation: https://pyturkce.readthedocs.io.

Installation

pip install pyturkce

Usage

from pyturkce import pyturkce

word = pyturkce.Sozluk('kalem')

print(word.anlamlar) # ['Yazma, çizme vb. işlerde kullanılan çeşitli biçimlerde araç', 'Resmî kuruluşlarda yazı işlerinin görüldüğü yer', 'Yontma işlerinde kullanılan ucu sivri veya keskin araç', 'Çeşit, tür', 'Bazı deyimlerde yazı', 'Yazar']

print(word.ornekler) # ['Kâğıt, kalem, mürekkep, hepsi masanın üstündedir.', 'Kalemindeki odacıya aylığını kırdırırmış.', 'Taşçı kalemi.', 'Oymacı kalemi.', 'Üç kalem erzak.', 'Beş kalem ilaç.', 'Kaleme almak.', 'Peyami Safa, edebiyatımızın usta kalemlerindendir.']

Commands

PyTurkce has also CLI support.

usage: pyturkce [-h] [-m] [-i] [-i] [-l] [-r NUM]

optional arguments:
  -h, --help            show this help message and exit
  -m, --meaning         ilgili kelimenin anlamini getirir
  -i, --idiom           ilgili kelime ile kullanilan deyimleri getirir
  -s, --sample          ilgili kelime ile kurulan cumle orneklerini getirir
  -r NUM, --random NUM  Num sayida random kelime getirir (default: 1)

To-Do's

Fiil, sifat, edat ayirma
Stop words (etkisiz kelimeleri ayirma)
random n sayida kelime getir
icinde, basinda veya sonunda belirli harfleri bulunan kelimeleri getir
kelimelerin icerdigi harf oranlarina gore hatali kelimeleri bulup duzelt
turkce kripto bolumleri ekle

Contributors

We welcome contributions that make PyTurkce better and improve the existing functionalities of the project.

Mert Cobanov (@cobanov)
Fethi Tekyaygil (@tekyaygilfethi)

Python package for Turkish Language.

Related tags

Overview

PyTurkce

Installation

Usage

Commands

To-Do's

Contributors

Owner

Mert Cobanov

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Python package for Turkish Language.

Related tags

Overview

PyTurkce

Installation

Usage

Commands

To-Do's

Contributors

Owner

Mert Cobanov

A fast Text-to-Speech (TTS) model. Work well for English, Mandarin/Chinese, Japanese, Korean, Russian and Tibetan (so far). 快速语音合成模型，适用于英语、普通话/中文、日语、韩语、俄语和藏语（当前已测试）。

Named-entity recognition using neural networks. Easy-to-use and state-of-the-art results.

Search with BERT vectors in Solr and Elasticsearch

Code Generation using a large neural network called GPT-J

NLP Overview

Datasets of Automatic Keyphrase Extraction

Constituency Tree Labeling Tool

Extract rooms type, door, neibour rooms, rooms corners nad bounding boxes, and generate graph from rplan dataset

本项目是作者们根据个人面试和经验总结出的自然语言处理(NLP)面试准备的学习笔记与资料，该资料目前包含 自然语言处理各领域的 面试题积累。

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NLTK Source

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