semvec/pypi-packages huggingface dataset:
dicta-onnx 1.0.9
Add diacritics to Hebrew text using Dicta model
Requires Python: >=3.11

# dicta-onnx

Add diacritics to Hebrew text using [Dicta model](https://huggingface.co/dicta-il/dictabert-large-char-menaked)

See [model card](https://huggingface.co/dicta-il/dictabert-large-char-menaked) on HuggingFace 🤗

## Features

- Fast: 0.1s per sentnece (macOS M1) 🚀
- Batching: Supports multiple sentences at once 📚
- User friendly: Add diacritics with just 2 lines of code ✨
- Lightweight: Runs with onnx without heavy dependencies 🛠️
- Dual mode: Output niqqud male (fully marked) and niqqud haser 💡

## Install

```console
pip install -U dicta-onnx
```

## Usage

1. Install the library
2. Download model from [model-files-v1.0](https://github.com/thewh1teagle/dicta-onnx/releases/model-files-v1.0) and put in the directory
3. Run one of the examples from [examples](examples) folder

## Play

You can play with dicta-onnx in this [HuggingFace Space](https://huggingface.co/spaces/thewh1teagle/add-diacritics-in-hebrew)

## Credits

Special thanks ❤️ to [dicta-il](https://huggingface.co/dicta-il/dictabert-large-char-menaked) for their amazing Hebrew diacritics model! ✨
XD-SVKCR 0.1.0
A library that contains (well literally) random stuff
Requires Python: >=3.8

--UNFINISHED--

Inclues SuperVectors, SuperVectors 2D, BlackBoxes

SVectors: Vectors that randomly change their value depending on the magic we give then (using SHA256)
SVectors2: A set of two SVectors that can perform more flexible functions
BlackBoxes: A box that can only return Success in an unhingly rare condition (ideal to troll your friends)

LICENSE: Public domain
...
PraisonAITest 0.0.1
A minimal and simple testing framework for Large Language Models (LLMs)
Requires Python: >=3.8

# PraisonAITest

A minimal and simple testing framework for Large Language Models (LLMs) using LiteLLM.

## Installation

```bash
pip install PraisonAITest
```

For development installation:

```bash
pip install -e ./
```

## Usage

```python
from praisonaitest import Test

# Example with string validation
test1 = Test(model="openai/gpt-4o-mini", instruction="What is the capital of France?", validate="Paris")
result = test1.run()

print(f"Test result: {'Passed' if result['passed'] else 'Failed'}")
print(f"Response: {result['response']}")
print(f"Latency: {result['latency']:.2f} seconds")
print(f"Token usage: {result['token_usage']}")

# Example with a validation function
def validate_france_response(response):
    """Custom validator that checks if response mentions Paris and France"""
    return "Paris" in response and "France" in response

# Create a test with function validation
test2 = Test(
    model="openai/gpt-4o-mini", 
    instruction="Tell me about the capital of France", 
    validate=validate_france_response
)
result2 = test2.run()
```

## Environment Variables

Create a `.env` file with your API keys:

```
# LiteLLM API Key (Required)
LITELLM_API_KEY=your_api_key_here

# Default OpenAI API Key (Optional, if using OpenAI models directly)
OPENAI_API_KEY=your_openai_api_key_here
```

## License

This project is licensed under the MIT License.