semvec/pypi-packages huggingface dataset:
phtokenizer 1.0.0
Dynamic hybrid tokenizer for micro-datasets
License: MIT
Requires Python: >=3.7

# PHTokenizer

Dynamic Hybrid Tokenizer optimized for micro-datasets (100–500 samples).

## Features
- Optimized for small datasets to prevent over-fragmentation.
- Hybrid strategy using atomic tokens and selective BPE.
- Low CPU and RAM usage with LRU cache.
- Supports JSON, CSV, and raw text formats.

## Quick Start
```python
from phtokenizer import DynamicHybridTokenizer, TokenizerConfig

config = TokenizerConfig(vocab_size=1000)
tokenizer = DynamicHybridTokenizer(config=config)

data = ["sample text one", "sample text two"]
tokenizer.fit(data)

tokens = tokenizer.tokenize("sample text")
print(tokens)
PGram 0.0.9
Blazingly fast start async telegram bot on top of the aiogram with postgres channels
License: MIT
Requires Python: >=3.12
Keywords: aiogram, xync-net, postgres, channels, signals

# PGram

### Install
```sh
pip install PGram
```

### Minimal code for running
```python
### router.py
from aiogram import Router

r = Router()

@r.message()
async def del_msg(msg):
    await msg.delete()
```

```python
### main.py
from asyncio import run
from PGram import Bot
from router import r

bot = Bot("bot:token", [r])
run(bot.start())
```
InceptionRender 0.0.2
A small python package
License: MIT
Requires Python: >=3.8

# Inception-Renderer-PyPi-Package

This repository contains the source code and documentation for the Inception Renderer Python package, which is available on PyPi. The Inception Renderer is a powerful tool for rendering any youtube videos and any web page over the Jupyter/Colab Notebook.

# How to run?
## Installation:

#### Create a conda environment

```bash
conda create -n inrenderer python=3.8 -y
```

```bash
conda activate inrenderer
```


```bash
pip install -r requirements_dev.txt
```