semvec/pypi-packages huggingface dataset:
linkml-model 1.10.0rc2
Metamodel schema, documentation, and specification for the Linked Open Data Modeling Language (LinkML)
License: MIT
Requires Python: <4.0,>=3.9
Keywords: linked data, linkml, metamodel

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# LinkML Model

Metamodel schema, documentation, and specification for the Linked Open Data Modeling Language (LinkML)

This documentation is best viewed through the generated web documentation:

- [https://w3id.org/linkml/](https://linkml.github.io/linkml-model/docs)

## Quick Links

- [Generated documentation](https://linkml.github.io/linkml-model/docs)
- [Specification](https://linkml.io/linkml-model/docs/specification/00preamble/)
- [Source YAML for metamodel](https://github.com/linkml/linkml-model/tree/main/linkml_model/model/schema)
- [Main LinkML site](https://linkml.io)

## About LinkML

LinkML is a modeling framework for building datamodels and related applications.

The best place to start discovering more about linkml is on the main LinkML website, [linkml.io/linkml](https://linkml.io/linkml)

LinkML is self-describing, and the underlying datamodel for describing data models is in LinkML. This repository contains that data model

## For Developers

See the [contributing docs](https://linkml.io/linkml/contributing/contributing.html)

### Installation

This project uses poetry:

```bash
> cd linkml-model
> poetry install
```

## Running tests

```bash
> make test
```
plp-antispam-rust 1.0.3
Fast Rust-based anti-spam inference engine for email classification
License: MIT
Requires Python: >=3.9

# plp-antispam-rust

Fast Rust-based anti-spam inference engine for email classification, achieving **10x speedup** over pure Python with 99.9% accuracy.

## Features

- **High Performance**: 2,300+ emails/second inference speed (vs 220 emails/s in Python)
- **Production Ready**: 99.9% accuracy on real-world spam datasets
- **Cross-Platform**: Works on Linux, macOS (Intel/ARM), and Windows
- **Python 3.9+**: Compatible with Python 3.9, 3.10, 3.11, 3.12, 3.13+ (abi3 stable ABI)
- **Zero Dependencies**: Self-contained binary wheels, no external dependencies

## Installation

```bash
pip install plp-antispam-rust
```

## Usage

```python
import plpas
from plpas import RustEnsemble

# Load pre-trained models
ensemble = RustEnsemble.load("path/to/models")

# Predict single email
prediction = ensemble.predict(["email text here"])
print(f"Spam probability: {prediction[0]}")

# Batch prediction (faster)
emails = ["email 1", "email 2", "email 3"]
predictions = ensemble.predict(emails)
```

## Architecture

4-model stacked ensemble:
- Model 1: Naive Bayes + Word Unigrams
- Model 2: Logistic Regression + Word Bigrams
- Model 3: SVM + Character Trigrams
- Model 4: XGBoost + Word 1-2 Grams
- Meta-Classifier: Logistic Regression with StandardScaler

All models are pre-trained and exported to MessagePack format for fast Rust deserialization.

## Performance

| Implementation | Speed (emails/s) | Accuracy |
|---------------|------------------|----------|
| Python (sklearn) | 220 | 99.93% |
| **Rust (this package)** | **2,311** | **99.90%** |
| **Speedup** | **10.5x** | **-0.03%** |

## License

MIT

## Author

Paul Lucian Pinzariu
ask-deepwiki 0.1.0
CLI tool for querying DeepWiki documentation
License: MIT
Requires Python: >=3.10
Keywords: cli, deepwiki, documentation, mcp

# DeepWiki CLI

A command-line tool for querying [DeepWiki](https://deepwiki.com) documentation for any GitHub repository.

DeepWiki provides AI-generated documentation for open source repositories. This CLI lets you explore documentation structure, read contents, or ask questions about any repository directly from your terminal.

## Installation

### Using uvx (recommended)

```bash
uvx ask-deepwiki --help
```

### Using pip

```bash
pip install ask-deepwiki
```

### From source

```bash
git clone https://github.com/4rays/ask-deepwiki
cd ask-deepwiki
uv sync
uv run ask-deepwiki --help
```

## Usage

### Get Documentation Structure

View the table of contents for a repository's documentation:

```bash
ask-deepwiki structure facebook/react
```

Output:
```
1. Overview
  1.1 Repository Purpose
  1.2 Key Technologies
2. Architecture
  2.1 Fiber Reconciler
  2.2 Scheduler
...
```

### Get Full Documentation

Retrieve the complete documentation contents:

```bash
ask-deepwiki contents vercel/next.js
```

Note: This may return a large amount of text for repositories with extensive documentation.

### Ask Questions

Ask natural language questions about a repository:

```bash
ask-deepwiki ask facebook/react "What is Fiber?"
```

Output:
```
Fiber is React's reconciliation algorithm introduced in React 16...
```

More examples:

```bash
ask-deepwiki ask langchain-ai/langchain "How do I create a chain?"
ask-deepwiki ask pytorch/pytorch "What optimizers are available?"
ask-deepwiki ask microsoft/vscode "How does the extension API work?"
```

## Commands

| Command | Description |
|---------|-------------|
| `ask-deepwiki structure <repo>` | Get documentation structure (table of contents) |
| `ask-deepwiki contents <repo>` | Get full documentation contents |
| `ask-deepwiki ask <repo> "<question>"` | Ask a question about the repository |
| `ask-deepwiki --version` | Show version |
| `ask-deepwiki --help` | Show help |

## Repository Format

All commands expect the repository in `owner/repo` format:

- `facebook/react`
- `vercel/next.js`
- `langchain-ai/langchain`
- `microsoft/typescript`

## Requirements

- Python 3.10+

## License

MIT