semvec/pypi-packages huggingface dataset:
materials-for-mc 0.2.0
Material builder for Monte Carlo simulations
License: MIT

[![Test usage with Python](https://github.com/fusion-neutronics/materials_for_mc/actions/workflows/ci-python.yml/badge.svg)](https://github.com/fusion-neutronics/materials_for_mc/actions/workflows/ci-python.yml)
[![Test usage with WASM](https://github.com/fusion-neutronics/materials_for_mc/actions/workflows/ci-wasm.yml/badge.svg)](https://github.com/fusion-neutronics/materials_for_mc/actions/workflows/ci-wasm.yml)
[![Test usage with Rust](https://github.com/fusion-neutronics/materials_for_mc/actions/workflows/ci-rust.yml/badge.svg)](https://github.com/fusion-neutronics/materials_for_mc/actions/workflows/ci-rust.yml)

# Materials for MC

Materials For MC is a package for accessing nuclear cross section data

See the 👉 **[documentation](https://fusion-neutronics.github.io/materials_for_mc/)** 👈 for installation and API usage details.

🦀Rust back end for speed
🐍Python API for ease of use
🕸️WASM API for online deployment
tagforge 0.1.0
An open-source tool that uses LLMs + dspy to automatically generate smart, context-aware tags for blog posts, articles, and other written content
License: MIT
Requires Python: <3.14,>=3.10

# 🏷️ TagForge

<p align="center">
  <img src="docs/assets/banner.png" alt="TagForge Logo" width="600"/>
</p>

**Forge meaningful tags from raw content with AI.**

TagForge is an open-source tool that uses **LLMs + dspy** to automatically generate smart, context-aware tags for blog posts, articles, and other written content.
It helps writers, developers, and teams improve discoverability and organization with **semantic tagging** powered by AI.

## Installation

TagForge prefers to use a local LLM for preserving PII information.

### Macos

```bash
brew install ollama
brew services start ollama
ollama pull llama3.1:8b
```

## Privacy Preservation (PAPILLON-Inspired)

This project implements a privacy-preserving pipeline inspired by:

- **PAPILLON: PrivAcy Preservation from Internet-based and Local Language Model Ensembles**
  Antoine Bosselut, et al.
  arXiv: [2410.17127](https://arxiv.org/abs/2410.17127)

Our implementation adapts their core methodology (local filtering + remote API model + evaluation on QUAL/LEAK) within a DSPy framework.