semvec/pypi-packages huggingface dataset:
mathykit 0.1.4
A lightweight AI framework for using Meta/Facebook models with pure Python
Requires Python: >=3.7
Keywords: ai, machine learning, nlp, meta, facebook, opt, transformers

# MathyKit

MathyKit is a lightweight Python framework for building and using AI models, with a focus on Meta/Facebook models. It provides a simple and efficient way to use state-of-the-art AI models without the need for API keys or local model installations.

## Features

- Easy-to-use interface for AI model integration
- Support for Meta/Facebook models
- Pure Python implementation
- No API keys required
- Lightweight and efficient

## Installation

```bash
pip install mathykit
```

## Quick Start

```python
from mathkit import Model
from mathkit.models import MetaOPT

# Load a pre-trained model
model = MetaOPT.from_pretrained("meta-opt-1.3b")

# Generate text
output = model.generate("What is artificial intelligence?")
print(output)
```

## Documentation

For detailed documentation and examples, visit our [documentation page](https://mathkit.readthedocs.io).

## License

This project is licensed under the MIT License - see the LICENSE file for details.
actsnclass 1.3.1
Active Learning for Supernova Photometric Classification
Requires Python: >=3.12

[![crp4](https://img.shields.io/badge/CRP-%234-blue)](https://iaacoin.wixsite.com/crp2017)
[![Paper DOI](https://img.shields.io/badge/Paper%20DOI-10.1093%2Fmnras%2Fsty3015-green)](https://doi.org/10.1093/mnras/sty3015) 
[![arXiv](https://img.shields.io/badge/arxiv-astro--ph%2F1804.03765-red)](https://arxiv.org/abs/1804.03765) 


# [<img align="right" src="docs/images/COIN_logo_very_small.png" width="350">](https://cosmostatistics-initiative.org/) ActSNClass


## Active Learning for Supernova Photometric Classification 

This repository holds the code and data used in [Optimizing spectroscopic follow-up strategies for supernova photometric classification with active learning](https://arxiv.org/abs/1804.03765), by [Ishida](https://www.emilleishida.com), [Beck](https://github.com/beckrob), [Gonzalez-Gaitan](https://centra.tecnico.ulisboa.pt/team/?id=4337), [de Souza](https://www.rafaelsdesouza.com), [Krone-Martins](https://thegrid.ai/alberto-krone-martins/), [Barrett](http://jimbarrett.co.uk/), [Kennamer](https://github.com/NobleKennamer), [Vilalta](http://www2.cs.uh.edu/~vilalta/), [Burgess](https://grburgess.github.io/), [Quint](https://github.com/b1quint), [Vitorelli](https://github.com/andrevitorelli), [Mahabal](http://www.astro.caltech.edu/~aam/) and [Gangler](https://annuaire.in2p3.fr/agents/Y249R2FuZ2xlciBFbW1hbnVlbCxvdT1wZW9wbGUsZGM9aW4ycDMsZGM9ZnI=/show), 2018.

This is one of the products of [COIN Residence Program #4](http://iaacoin.wix.com/crp2017), which took place in August/2017 in Clermont-Ferrand (France). 

We kindly ask you to include the full citation if you use this material in your research: [Ishida et al, 2019, MNRAS, 483 (1), 2–18](https://ui.adsabs.harvard.edu/abs/2019MNRAS.483....2I/abstract).

Full documentation can be found at [readthedocs](https://actsnclass.readthedocs.io/en/latest/index.html#).

# Dependencies

### For code:

 - Python>=3.7  
 - argparse>=1.1  
 - matplotlib>=3.1.1  
 - numpy>=1.17.0  
 - pandas>=0.25.0  
 - setuptools>=41.0.1  
 - scipy>=1.3.0
 - scikit-learn>=0.20.3
 - seaborn>=0.9.0
 - xgboost>=1.6.2
 
 
 ### For documentation:
 
  - sphinx>=2.1.2

# Install

The current version runs in Python-3.7 or latter.

We recommend you use a [virtual environment](https://packaging.python.org/en/latest/guides/installing-using-pip-and-virtual-environments/) to ensure the correct package versions.

Once your environment is created, you can source it :  
    >> source <path_to_venv>/bin/activate
    
You will notice a `(ActSNCLass)` to the left of your terminal line.
This means everything is ok!

In order to install this code you should clone this repository and do::  

    (ActSNClass) >> pip install --upgrade pip
    (ActSNClass) >> pip install -r requirements.txt
    (ActSNClass) >> pip install .