تولید اسم های رندوم فینگیلیش

Last update: Nov 24, 2022

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Overview

karafs کرفس

تولید اسم های رندوم فینگیلیش

installation

➜ pip install karafs

usage

دو زبانه

➜ karafs -n 10
توت فرنگی بی ناموس
toot farangi-ye bi_namoos
غورهٔ خسته
ghoore-ye khaste
انار زره پوش
anaar-e zereh push
انگور سر سپرده
angoor-e sar sepordeh
زلزلک نادان
zalzalak-e naadaan
سیب ورپریده
sib-e varparideh
فندق عقدهای
fandogh-e oghde_ee
گریپ فروت قزمیت
grip froot-e ghozmit
نارگیل پرورده
nargil-e parvardeh
تن ماهی تافته
ton maahi-ye tafteh

فقط انگلیسی

➜ karafs -n 10 -en
shaftaaloo-ye boland parvaz
baalang-e khoshhal
porteghal-e foroo_heshteh
piyaz-e oghde_ee
fandogh-e afsordeh
gheisi-e kar azmoodeh
piyaz-e gomashteh
limoo shirin-e zhoolideh
senjed-e tafteh
ananas-e khafan

فقط فارسی

➜ karafs -n 10 -fa

کیوی شارلاتان
لوبیا چیتی چابک
نوشابهٔ مفلس
تن ماهی اسکل
انار آلوده
انجیر پرورده
توت خسته
خیار رهیده
کمبوزهٔ شراب زده
گلابی فرسوده

option `-n`

با این آپشن میتونید تعداد دفعات تولید اسم رندوم رو مشخص کنید. اگر تنظیمش نکنید به طور پیشفرض تعداد 1 در نظر گرفته میشه.

➜ karafs -n 5

option `--no-space`

با این آپشن میتونید خروجی رو بدون فاصله بین کلمات بگیرین:

➜ karafs --no-space -n 10

use as library

شما میتونید کرفس رو به عنوان یک کتابخانه توی برنامه هاتون استفاده کنید.

import karafs

print(karafs.gen_str()) # generate a random name (finglish)
print(karafs.gen_str('fa')) # generate a random name (persian)

print(karafs.gen_str_without_space()) # generate a random name without whitespace (finglish)
print(karafs.gen_str_without_space('fa')) # generate a random name without whitespace (persian)

Comments

استفاده به عنوان کتابخانه

در حال حاضر میشه این ابزار رو فقط توی خط فرمان استفاده کرد. @iamvee ایده ای برای اینکه مثلا بشه ایمپورتش کرد و اسم های رندوم رو تو برنامه استفاده کرد نداری؟
enhancement

opened by parsampsh 2
Some cleanups
Hi there. I maked this changes:

added docstring for main

removed from secrets import choice because function choice is declared

added docstring for function choice

fixed some trailing whitespaces

cleanup function gen

added .gitignore
opened by parsampsh 2
Use as library
فایل bin/karafs رو به karafs/karafs.py منتقل کردم. و توی bin/karafs هم کرفس رو ایمپورت کردم و اجراش کردم. اینجوری بقیه هم میتونن خارج از خط فرمان تو کدشون استفادش کنن. تست مست و داکیمونت هم براش اضافه کردم. (#9)

همچنین میتونن اینجوری هم رانش کنن:

$ python3 -m karafs
opened by parsampsh 1
Add GitHub workflow

I Added github workflow for running the tests. But there is a problem in Windows (I think its related to printing persian letters in the terminal). I disabled windows.

opened by parsampsh 0
Cleanup
removed unused random import

added docstrings

fix some trailing whitespaces

handle using function gen

some small refactors

downgrade python version to 3.6 in setup.py (this also works in 3.6)
opened by parsampsh 0
Update karafs.py - replace some underlines with dashes

Hi, I replaced some underlines in adjectives with dashes, because it was returning some names containing both underline and dash and it was not beautiful. (for example: hendoone-ye-afsordeh_sima)

opened by erfansaberi 0

Releases(v0.1)

v0.1(Feb 13, 2021)

Source code(tar.gz)
Source code(zip)
v0.0.3(Feb 11, 2021)

Source code(tar.gz)
Source code(zip)

Owner

Vaheed NÆINI (9E)

جالینوکس　　 𝚊𝚕𝚒𝚊𝚜 𝚎="𝚎𝚖𝚊𝚌𝚜 -𝚗𝚠"

GitHub Repository https://iamv.ir/karafs

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تولید اسم های رندوم فینگیلیش

Related tags

Overview

karafs کرفس

installation

usage

دو زبانه

فقط انگلیسی

فقط فارسی

option -n

option --no-space

use as library

Comments

استفاده به عنوان کتابخانه

Some cleanups

Use as library

Add GitHub workflow

Cleanup

Update karafs.py - replace some underlines with dashes

Releases(v0.1)

v0.1(Feb 13, 2021)

v0.0.3(Feb 11, 2021)

Owner

Vaheed NÆINI (9E)

Code for paper "Which Training Methods for GANs do actually Converge? (ICML 2018)"

Cải thiện Elasticsearch trong bài toán semantic search sử dụng phương pháp Sentence Embeddings

An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library.

Amazon Multilingual Counterfactual Dataset (AMCD)

FedNLP: A Benchmarking Framework for Federated Learning in Natural Language Processing

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Learn meanings behind words is a key element in NLP. This project concentrates on the disambiguation of preposition senses. Therefore, we train a bert-transformer model and surpass the state-of-the-art.

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option `-n`

option `--no-space`