Metadata-Version: 2.4
Name: zetnlp
Version: 1.0.0
Home-page: https://github.com/prasenjeett/ZetNLP
Author: Prasenjeet Howlader
Author-email: prasenjeethowlader122@gmail.com
License: MIT
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: nltk
Requires-Dist: spacy
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: license
Dynamic: license-file
Dynamic: requires-dist

# ZetNLP - Multilingual Natural Language Processing Library

ZetNLP is a multilingual NLP library built from scratch, designed to provide an extensive range of natural language processing capabilities for multiple languages. 

## Features

- **Multilingual Support**: Process text in multiple languages with ease.
- **Tokenization**: Efficiently tokenize text into words, sentences, and more.
- **Part-of-Speech Tagging**: Identify parts of speech in text.
- **Named Entity Recognition**: Detect and classify named entities in text.
- **Sentiment Analysis**: Analyze the sentiment of text data.
- **Text Classification**: Classify text into predefined categories.
- **Text Summarization**: Generate concise summaries of text data.
- **Language Detection**: Automatically detect the language of text data.

## Installation

To install ZetNLP, simply run:

```bash
pip install zet-nlp
```

## Usage

Below is a basic example of how to use ZetNLP:

```
from znlp import ZetNLP

# Initialize ZetNLP for Bangla language
nlp = ZetNLP(language='bangla')

# Process the text
doc = nlp("ঢাকায় আজ খুব গরম।")

# Print token information
for token in doc.tokens:
    print(f"{token.text} => POS: {token.pos}, Lemma: {token.lemma}, Entity: {token.entity}")

```
## Use for multilingual 
```
from zet-nlp import ZetNLP

# Initialize ZNLP for English language
nlp = ZetNLP(language='english')

# Process the text
doc = nlp("The weather is very hot in New York today.")

# Print token information
for token in doc.tokens:
    print(f"{token.text} => POS: {token.pos}, Lemma: {token.lemma}, Entity: {token.entity}")
```
## Documentation

Comprehensive documentation is available at [ZNLP Documentation](https://znlp-docs.example.com).

## Contributing

We welcome contributions from the community! Please read our [Contributing Guidelines](CONTRIBUTING.md) for more information on how to get started.

## License

ZNLP is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.

## Contact

For any questions or inquiries, please contact us at [support@znlp.com](mailto:support@znlp.com).
