semvec/pypi-packages huggingface dataset:
aamraz 0.1.0
This project is a collection of Natural Language Processing tools for Kurdish Language.
Requires Python: >=3.6

# Aamraz - Kurdish NLP collection

## Overview
Aamraz which is written "ئامراز" in kurdish script means "instrument". This project is a collection of Natural Language Processing tools for Kurdish Language.
Despite being spoken by millions, Kurdish remains an under-resourced language in Natural Language Processing (NLP).
Recognizing the rich cultural heritage and historical significance of the Kurdish people, we—regardless of ethnicity—are committed to advancing tools and pre-trained models that empower the Kurdish language in modern research and technology.
Our work aims to foster further development and provide a foundation for future research and applications in NLP. [see github repository](https://github.com/MohammadDevelop/Aamraz)


## Installation
    pip install aamraz

## Base Features
- **Normalization** 
- **Tokenization** 
- **Stemming**
- **Word Embedding:** Creates vector representations of words.
- **Sentences Embedding:** Creates vector representations of sentences.

## Usage
```python
import aamraz

# Normalization
normalizer= aamraz.Normalizer()
sample_sentence="قڵبە‌کە‌م‌ بە‌  کوردی‌  قسە‌ دە‌کات‌."
normalized_sentence=normalizer.normalize(sample_sentence)
print(normalized_sentence)

# Tokenization
tokenizer = aamraz.WordTokenizer()
sample_sentence="زوانی له دربره"
tokens = tokenizer.tokenize(sample_sentence)
print(tokens)

# Embedding by fasttext
model_path = 'kurdish_fasttext_skipgram_dim300_v1.bin'
embedding_model = aamraz.EmbeddingModel(model_path, dim=50)

sample_word="ئامراز"
sample_sentence="زوانی له دربره"

word_vector = embedding_model.word_embedding(sample_word)
sentence_vector = embedding_model.sentence_embedding(sample_sentence)

print(word_vector)
print(sentence_vector)

# Embedding by word2vec
model_path = 'kurdish_word2vec_model_dim100_v1.bin'
embedding_model = aamraz.EmbeddingModel(model_path, type='word2vec')

sample_word="ئامراز"
sample_sentence="زوانی له دربره"

word_vector = embedding_model.word_embedding(sample_word)
sentence_vector = embedding_model.sentence_embedding(sample_sentence)

print(word_vector)
print(sentence_vector)

# Stemming
stemmer=aamraz.Stemmer(method='simple')
stemmed=stemmer.stem("کتێبەکانمان")
print(stemmed)
```
python-wheel-to-conda-package 0.4.1
Convert a Pure-Python Wheel to a noarch Conda package
Requires Python: >=3.10
Keywords: conda, conda-package, python-wheel, wheel

<a href="https://pypi.org/project/python-wheel-to-conda-package" target="_blank">
  <img src="https://img.shields.io/pypi/v/python-wheel-to-conda-package" alt="Package version">
</a>

# Python Wheel to Conda package

This converts a Pure-Python Wheel to a noarch Conda package.

This tool can be used to replace `conda build` which can sometimes be very slow.
[`setup.py bdist_conda`](https://docs.conda.io/projects/conda-build/en/latest/user-guide/recipes/build-without-recipe.html) can build a Conda package without a recipe but it relies on conda-build behind the scenes and thus suffers from the same slowness.

## Usage

### As a library

```python
from python_wheel_to_conda_package import python_wheel_to_conda_package

conda_package_path = python_wheel_to_conda_package(wheel_path, output_directory=some_directory)
```

### As a command line tool

```console
$ python-wheel-to-conda-package test_lib-0.4.2.dev0-42_1337gg-py3-none-any.whl --output-directory /a/b/c/
/a/b/c/test-lib-0.4.2.dev0-1337gg.tar.bz2
```
pipewizard 0.0.1
A tool to help generate GitHub pipelines for Python projects
Requires Python: >=3.10

# Pipewizard - WORK IN PROGRESS

Pipewizard is a CLI tool that helps you generate GitHub pipelines for Python projects. You can select different Python versions and tasks like testing, linting, and Dockerizing.

## Installation

To install the tool, run:

```bash
pip install pipewizard
```

## Usage

```bash
pipewizard
```