semvec/pypi-packages huggingface dataset:
paramak 0.9.8
Create 3D fusion reactor CAD models based on input parameters
Requires Python: >=3.8
Keywords: python, geometry, reactor, model, cad, fusion, parametric, dagmc, openmc


[![N|Python](https://www.python.org/static/community_logos/python-powered-w-100x40.png)](https://www.python.org)

[![CI testing](https://github.com/fusion-energy/paramak/actions/workflows/ci.yml/badge.svg)](https://github.com/fusion-energy/paramak/actions/workflows/ci.yml)

[![documentation release](https://github.com/fusion-energy/paramak/actions/workflows/documentation_update.yml/badge.svg)](https://github.com/fusion-energy/paramak/actions/workflows/documentation_update.yml)

[![PyPI release](https://github.com/fusion-energy/paramak/actions/workflows/python-publish.yml/badge.svg)](https://github.com/fusion-energy/paramak/actions/workflows/python-publish.yml)


# Paramak

Paramak python package allows rapid production of 3D CAD models and neutronics
models of fusion reactors.

The purpose of Paramak is to provide geometry for parameter studies and therefore the models are created from parameters.

Paramak produces [CadQuery](https://github.com/CadQuery/CadQuery) objects which can easily be saved to CAD files in several formats (STEP, STL, BRep) and also readily convertable to DAGMC h5m neutronics geometry using tools such as [Cad-to_DAGMC](https://github.com/fusion-energy/cad_to_dagmc).

:point_right: Please see the [online documentation](https://fusion-energy.github.io/paramak) for installation, usage and API documentation.
T5SummaryPratik 0.1.0
A complete text summarization pipeline using T5 with evaluation metrics
Requires Python: >=3.8
Keywords: text summarization, t5, nlp, machine learning

# T5SummaryPratik

A powerful text summarization library using T5 transformer model with built-in evaluation metrics.

## Installation

```bash
pip install T5SummaryPratik
```

## Features

- Text summarization using T5 transformer model
- Multiple evaluation metrics:
  - ROUGE scores
  - Readability metrics
  - Text statistics
- Easy to use API
- Customizable parameters

## Quick Start

```python
from T5SummaryPratik import T5Summarizer

# Initialize the summarizer
summarizer = T5Summarizer()

# Example text
text = """
Your long text here that needs to be summarized.
This can be multiple paragraphs long.
"""

# Generate summary
summary = summarizer.summarize(text)
print(summary)

# Get summary with evaluation metrics
summary_with_metrics = summarizer.summarize_with_metrics(text)
print(f"Summary: {summary_with_metrics['summary']}")
print(f"ROUGE scores: {summary_with_metrics['metrics']['rouge']}")
print(f"Readability score: {summary_with_metrics['metrics']['readability']}")
```

## Advanced Usage

See [example_usage.py](example_usage.py) for more advanced usage examples.

## Requirements

- Python >= 3.8
- PyTorch
- Transformers
- NLTK
- Evaluate
- Textstat

## License

MIT License

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.