semvec/pypi-packages huggingface dataset:
wgus 0.0.12
API for Wargaming Update Service (WGUS)
Requires Python: >=3.8

[![Test](https://github.com/IzeBerg/wgus/actions/workflows/test.yml/badge.svg)](https://github.com/IzeBerg/wgus/actions/workflows/test.yml)
[![codecov](https://codecov.io/gh/IzeBerg/wgus/branch/main/graph/badge.svg?token=FMXTYNXAIH)](https://codecov.io/gh/IzeBerg/wgus)
<a href="https://pypi.org/project/wgus" target="_blank">
    <img src="https://img.shields.io/pypi/v/wgus?color=%2334D058&label=pypi%20package" alt="Package version">
</a>
<a href="https://pypi.org/project/wgus" target="_blank">
    <img src="https://img.shields.io/pypi/pyversions/wgus.svg?color=%2334D058" alt="Supported Python versions">
</a>

API for Wargaming Update Service (WGUS)
xtop-cli 1.0.9
A lightweight monitor for FPS, System Usage and Docker Stats
Requires Python: >=3.8

# xtop
A terminal monitor fos FPS, System Usage and Docker Stats

Make sure to have the latest version of PyPA’s build and Twine installed:
```angular2html
python3 -m pip install --upgrade build twine
```

Now run this command from the same directory where pyproject.toml is located:
```angular2html
python3 -m build
```

Run Twine to upload all of the archives under dist:
```angular2html
python3 -m twine upload --repository testpypi dist/*
```

You can use pip to install your package and verify that it works. Create a virtual environment and install your package from TestPyPI:
```angular2html
python3 -m pip install --force-reinstall --index-url https://test.pypi.org/simple/ --no-deps xtop-cli
<!--python3 -m pip install &#45;&#45;force-reinstall &#45;&#45;index-url https://test.pypi.org/simple/ xtop-cli-->
```

Once tested, run Twine to upload all of the archives under dist. Now that you’re uploading the package in production, you don’t need to specify --repository; the package will upload to https://pypi.org/ by default.:
```angular2html
python3 -m twine upload dist/*
```
formdt 0.6.0
An opinionated markdown formatter
Requires Python: >=3.13

# formdt

[![CI](https://github.com/sanchitram1/formdt/actions/workflows/ci.yml/badge.svg)
](https://github.com/sanchitram1/formdt/actions/workflows/ci.yml)
[![Coverage Status](https://coveralls.io/repos/github/sanchitram1/formdt/badge.svg?branch=admin)
](https://coveralls.io/github/sanchitram1/formdt?branch=admin)

Markdown formatter with configurable line length.

## Installation

```bash
uv add formdt
```

## Usage

### CLI

```bash
# Print formatted output to stdout
formdt README.md

# Write changes back to file
formdt README.md --write

# Override line length
formdt README.md --line-length 120
```

### Jupyter Notebooks

> [!note] 
> You must specify either `-m` (all markdown cells) or `-c` (specific cells) when
> formatting notebooks.

```bash
# Format all markdown cells
formdt notebook.ipynb -m -w

# Format specific cells (0-indexed)
formdt notebook.ipynb -c "0,2,5" -w

# Format a range of cells
formdt notebook.ipynb -c "1-3,7" -w

# Preview without writing
formdt notebook.ipynb -m -l 65
```

### Library

```python
from formdt import format_markdown, Config

text = "This is a very long line that needs to be wrapped."
config = Config(line_length=40)

formatted = format_markdown(text, config)
```

## Configuration

Create a `.formdt` file in your project root:

```json
{
    "line_length": 80
}
```

## Rules

- **Line wrapping**: Lines are wrapped at the configured length
- **Single line breaks**: Joined within paragraphs
- **Double line breaks**: Preserved as paragraph separators
- **Links**: ` [text](url) ` patterns are kept intact and never broken across lines
  - **Previews**: Previews are also kept intact and never broken across lines
- **Preserved blocks**: Headings, lists, code fences, and math blocks (`$$`) are not
  modified

## Development

```bash
# Install dependencies
uv sync

# Run tests
uv run pytest
```

## Tasks

### test

```bash
pytest
```

### sync

```bash
uv sync --all-extras
uv pip install -e .
```

### cov

```bash
uv run coveralls
```
modelhubxc-tool 0.1.3
A command-line tool for downloading models from ModelScope and Hugging Face platforms
Requires Python: >=3.9

# modelhubxc-tool

一个用于从 ModelScope 和 Hugging Face 等平台下载模型的命令行工具。

## 简介

`modelhubxc-tool` 是一个命令行工具，旨在简化从 ModelScope 和 Hugging Face 等模型平台下载模型的过程。它支持从多个平台下载模型，并提供了便捷的查询功能来获取模型信息。

## 功能

- 从 ModelScope 和 Hugging Face 下载模型
- 查询模型信息
- 支持批量下载（通过 CSV 文件）
- 配置文件管理
- 可自定义下载路径和缓存目录

## 安装

使用 pip 安装：

```bash
pip install modelhubxc-tool
```

## 使用方法

### 1. 下载模型

从 ModelScope 下载模型：

```bash
modelhubxc-tool download --source modelscope owner/model-name
```

从 Hugging Face 下载模型：

```bash
modelhubxc-tool download --source huggingface owner/model-name
```

### 2. 查询模型信息

查询模型的详细信息：

```bash
modelhubxc-tool query owner/model-name
```

### 3. 批量下载

通过 CSV 文件批量下载模型，CSV 文件格式为：

```
model-id,source
owner/model1,modelscope
owner/model2,huggingface
```

批量下载命令：

```bash
modelhubxc-tool download-by-csv path/to/your/csv/file.csv
```

### 4. 查看版本

查看工具版本：

```bash
modelhubxc-tool --version
```

## 配置

工具会在 `~/.config/modelhubxc/config.yaml` 创建配置文件，包含以下可配置项：

- `download_dir`: 下载目录路径
- `cache_dir`: 缓存目录路径
- `max_workers`: 最大并发下载线程数
- `cookie_file`: Cookie 文件路径
- `token`: 认证令牌

首次运行时，如果下载目录未配置，工具会提示输入下载路径。

## 依赖

- typer (>=0.20.1,<0.21.0)
- pyyaml (>=6.0.3,<7.0.0)
- requests (>=2.31.0,<3.0.0)
- rich (>=13.0.0,<14.0.0)
- modelscope (>=1.33.0,<2.0.0)
- huggingface-hub (>=1.2.3,<2.0.0)

## 开发

```bash
git clone <repository-url>
cd modelhubxc-tool
pip install -e .
```

## 许可证

此项目采用 MIT 许可证 - 详情请参阅 [LICENSE](LICENSE) 文件。
nerna 0.2.1
NERNA is a lightweight tool for annotating named entities directly within Python notebooks, ideal for quick and interactive NER tasks without the need for deployment or external servers.
Requires Python: >=3.0

# NERNA (NER Notebook Annotation)

Follow the official repository: [NER-Notebook-Annotation - GitHub](https://github.com/danttis/NER-Notebook-Annotation/) 	


**NERNA** is a lightweight package designed for **Named Entity Recognition (NER) annotation** directly within Python notebooks.

Originally intended as a Streamlit-based interface, it has been reworked to run natively inside notebook environments (such as Jupyter, Google Colab, Databricks, etc.). This makes it easier to use without requiring deployment of web applications or cloud server contracts.

## Key Features

* ✅ Lightweight, interactive JavaScript interface embedded in notebooks
* ✅ Compatible with local notebooks and cloud platforms (e.g., Colab, Databricks)
* ✅ No need for external servers or deployments
* ⚠️ Annotations are made using **JavaScript**, so **they cannot be accessed directly as Python variables**. However, the input to the tool must be a **Python list of strings**.

---

## Usage Example

```python
from nerna import NERAnnotator

# List of texts to annotate
texts = [
    'Brazil won the 2002 World Cup.',
    'The planet’s drinking water is running out.'
]

# Initialize annotation
annotator = NERAnnotator(texts)

# Render the interactive annotation interface
annotator.render()
```
![NERNA Screenshot](https://raw.githubusercontent.com/danttis/NER-Notebook-Annotation/refs/heads/main/docs/img/image.png)

---

## Notes

4. **Retrieve annotations:**

   There are two ways to retrieve the annotated data back into Python:

   **Option A: Export to Python Variable (Recommended for Colab/Jupyter)**
   
   Pass the name of your variable to the `render` method:
   ```python
   # 1. Initialize
   annotator = NERAnnotator(texts)
   
   # 2. Render with variable name
   annotator.render(variable_name="annotator")
   ```
   *   In the UI, click the **"🐍 Export to Python"** button.
   *   Access the data in Python:
   ```python
   # After clicking the button:
   print(annotator.annotations)
   ```

   **Option B: Load from JSON (Fallback)**
   
   *   Click **"📥 Download All"** in the UI to save a `.json` file.
   *   Load it in Python:
   ```python
   from nerna import load_annotations_from_json
   
   data = load_annotations_from_json("path/to/all_annotations_....json")
   print(data)
   ```

* Annotated results are not automatically returned to Python unless you use the "Export to Python" button.
* Ideal for manual review, small-scale labeling tasks, or quick experimentation in NLP workflows.