semvec/pypi-packages huggingface dataset:
SentimentAnalysisDarijaNZ 0.3.5
C'est une bibliothèque Python modulaire qui fournit des outils pour le calcul de similarité, le filtrage de données, et l'analyse de sentiments.
Requires Python: >=3.6


# SentimentAnalysisDarijaNZ

**SentimentAnalysisDarijaNZ** is a modular Python library designed for text processing in Moroccan Darija (Arabic dialect). The library provides tools for similarity calculation, filtering, and sentiment analysis.

## Key Features

### 1. **Similarity Calculation**
This module includes advanced similarity measures tailored for Moroccan Darija. It provides functions to compute similarity scores between words or phrases while handling linguistic nuances such as:
- **Levenshtein Similarity:** Accounts for minor variations in spelling.
- **Phonetic Similarity:** Matches words based on their phonetic resemblance.
- **Sequence Similarity:** Uses sequence matching to evaluate structural similarity.

Example:
```python
from SentimentAnalysisDarijaNZ import similarity
similarity_score = similarity.levenshtein_similarity("makla", "maakla")
print(similarity_score)
```

### 2. **Filtering**
This module identifies and filters text based on specific brands, quality indicators, or prices. It also includes spam detection tailored for Moroccan Darija.

**Spam Filtering Example:**
```python
from SentimentAnalysisDarijaNZ import filtering
positive_spam, neutral_phrases = filtering.spam_analysis(["Check this link www.fake.com", "Free money!"], spam_dict)
print(positive_spam)
```

**Brand Filtering Example:**
```python
filtered, not_filtered = filtering.filterbrand([["laptop of good quality"]], "quality")
print(filtered)
```

### 3. **Sentiment Analysis**
Detects positive and negative sentiments in Darija text, accounting for nuances like negations and intensifiers.

Example:
```python
from SentimentAnalysisDarijaNZ import sentiment
positive, negative, neutral = sentiment.sentiment_analysis_darija(["The product is amazing!", "Terrible quality"])
print(positive)
```

## Installation

Install the library via pip:
```bash
pip install SentimentAnalysisDarijaNZ
```

## Usage

After installation, import the necessary modules:
```python
from SentimentAnalysisDarijaNZ import similarity, filtering, sentiment
```

## License

This project is licensed under the Apache License 2.0.



## Licence

Ce projet est sous licence MIT. Veuillez consulter le fichier `LICENSE` pour plus d'informations.

## Auteur
  **Nawfal BENHAMDANE**
zarinpalapi 0.1
Working library with Zarin Pal payment gateway services
Requires Python: >=3.6

Python Payment Gateway Library
Overview
This library provides an easy-to-use interface for integrating online payment gateways into your Python projects. It supports essential features such as generating payment URLs, verifying payments, and querying transaction statuses.

The library is written using asynchronous Python (async/await), ensuring high performance for applications handling multiple payment operations.

Features
Generate secure payment links for supported gateways.
Verify payments with transaction details.
Query transaction status for historical records.
Supports popular payment gateways (e.g., Zarinpal, NextPay, etc.).
Lightweight and easy to integrate.
Installation
You can install the library via pip (once published to PyPI):

bash
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pip install my_payment_gateway
For development purposes, you can clone this repository and install locally:

bash
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git clone https://github.com/yourusername/my_payment_gateway.git
cd my_payment_gateway
pip install -e .
Usage Example
Here’s a quick example to generate a payment link and verify the payment:

python
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import asyncio
from my_payment_gateway import PaymentGateway

async def main():
    # Initialize the gateway
    gateway = PaymentGateway(api_key="your_api_key", callback_url="https://yourcallback.url")

    # Generate a payment link
    payment_url = await gateway.create_payment(amount=100000, description="Test payment")
    print(f"Payment URL: {payment_url}")

    # Verify a transaction
    is_verified, details = await gateway.verify_payment(transaction_id="123456789")
    if is_verified:
        print("Payment verified:", details)
    else:
        print("Payment verification failed:", details)

asyncio.run(main())
Supported Gateways
The library currently supports the following payment gateways:

Zarinpal
NextPay
[Add more as needed]
Configuration
You can configure the library with the following parameters:

Parameter	Description
api_key	Your gateway API key (required).
callback_url	The URL to redirect users post-payment.
sandbox	Use the sandbox mode for testing (default: False).
Contributing
Contributions are welcome! If you’d like to contribute, please:

Fork the repository.
Create a new branch for your feature or bug fix.
Submit a pull request with a clear description of your changes.
License
This library is distributed under the MIT License. See the LICENSE file for more information.

Support
If you encounter any issues or have questions, feel free to create an issue in the GitHub repository.
lemmatizer_be 1.7.2
Lemmatizer for Belarusian language (based on bnkorpus.info)
Requires Python: >=3.8

Лематызатар на аснове Беларускага N-корпуса
===

## Як з ім працаваць?

Каманда ўстаноўкі:

`pip install lemmatizer_be`

Прыклад кода:

```python
from lemmatizer_be import BnkorpusLemmatizer

lm = BnkorpusLemmatizer()
print(lm.lemmatize("Мінску"))
print(lm.lemmatize("алоўка", pos="N"))
```

Можна таксама запусціць лематызатар як сервер:

`lemmatizer_be_server`

Дакументацыя па ім будзе даступна па адрасе `/docs` у браўзеры, калі вы запусціце сервер.

## Падзякі

Дзякуй праекту "Беларускі N-корпус" і асабліва Алесю Булойчыку, без іх гэты праект быў бы немагчымы.