semvec/pypi-packages huggingface dataset:
uzbek-lemmatization 2
Finds the lemma of Uzbek words
Requires Python: >=3.6

# Authors

**Author1: [Maksud Sharipov](https://github.com/MaksudSharipov)**

**Author2: [Dasturbek](https://github.com/ddasturbek)**

# Lemma & Lemmatization
There are 4 different ways of lemmatization: **rule**, **dictionary**, **model**, **hybrid**.

The package finds lemmas of Uzbek words based on the dictionary.

# Install

```bash
pip install uzbek_lemmatization
```

# Usage

```Python
import uzbek_lemmatization as ul

print(ul.lemmatize("qishlog‘imizda")) # qishloq
```
warpdatasets 3.0.14
Remote-first dataset library for ML training
Requires Python: >=3.10

# WarpDatasets v3

Remote-first dataset library for ML training.

## Installation

```bash
pip install warpdatasets
```

## Quick Start

```python
import warpdatasets as wd

# Load a dataset
ds = wd.dataset("workspace/name")

# Get a table
table = ds.table("main")

# Preview data
print(table.head(5))

# Get schema
print(table.schema())
```

## CLI

```bash
# Show dataset info
warpdata info workspace/name

# Show schema
warpdata schema workspace/name

# Preview rows
warpdata head workspace/name -n 10
```
ml-d2l-torch 1.0.7
ml_d2l_torch

# ml_d2l_torch

This repository contains PyTorch implementations of the Deep Learning book "Dive into Deep Learning" (D2L).   
It provides code examples, tutorials, and exercises to help you learn deep learning concepts using PyTorch.

## Installation

```
pip install -U ml_d2l_torch
```

## Code Examples

You can find the code examples in the `ml_d2l_torch` package. Here is a simple example of how to use it:

```python
from ml_d2l_torch import d2l_torch as d2l


```


## Build & upload to pypi
prerequirement: twine is installed. If not, run the following command to install it:
```bash
pip install -U twine
```

build and upload:
```bash
python setup.py sdist

twine upload dist/*
```
icx 1.0.2
This package provides the functionality for an interactive Streamlit dashboard designed to support stakeholders in exploring individual fairness notions within algorithmic decision-making systems. 
Requires Python: >=3.0

# Individual Consistency eXplorer (icx) Python Package

This package provides the functionality for an interactive Streamlit dashboard designed to support stakeholders in exploring individual fairness notions within algorithmic decision-making systems.

The dashboard allows users to:
* Explore and operate on a tabular dataset of individuals provided with their corresponding binary classifications;
* Define how similarity between individuals is measured, by configuring categorisation of attributes and how distances between attribute values are computed;
* Compute and visualise five individual fairness metrics that summarise the consistency of classifications across the dataset; and
* Inspect attributes of specific individuals and of those individuals most similar to them, to explore variations in attribute values and allow like-for-like comparisons of classifications.

This package implements the functionality described in a ECAI Demo Track paper 2025, available at [Individual Consistency eXplorer (ICX): An Interactive Dashboard for the Exploration of Individual Fairness](https://kclpure.kcl.ac.uk/ws/portalfiles/portal/345016883/ECAI-25-Demos_paper_71.pdf)

To see an online version of the dashboard, see [Individual Consistency eXplorer Online](https://individual-consistency-explorer.streamlit.app).

To see an online version of the dashboard, see [Individual Consistency eXplorer Online](https://individual-consistency-explorer.streamlit.app).


## 📦 Installation

It is recommended to install icx in a virtual environment (e.g., conda).

```
pip install icx
```

## Basic Usage

```
from icx import dashboard
```

### Run the dashboard

```
dashboard.run()
```

In the dashboard there is the ability to upload your own datasets or use the demo datasets provided.


Shield: [![CC BY-NC-SA 4.0][cc-by-nc-sa-shield]][cc-by-nc-sa]

This work is licensed under a
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa].

[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa]

[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/
[cc-by-nc-sa-image]: https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png
[cc-by-nc-sa-shield]: https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg
nonebot-plugin-maimaidx 2.1.11
maimaidx plugin for nonebot2
Requires Python: >=3.9

<div align='center'>

<a><img src='https://raw.githubusercontent.com/Yuri-YuzuChaN/nonebot-plugin-maimaidx/master/favicon.png' width='200px' height='200px' akt='maimaidx'></a>

[![python3](https://img.shields.io/badge/python-3.9+-blue.svg)](https://www.python.org/)
[![QQGroup](https://img.shields.io/badge/QQGroup-Join-blue)](https://qm.qq.com/q/gDIf3fGSPe)
[![License](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)

</div>


## 重要更新

## 温馨提示

**首次使用请务必看完 `README.MD` 所有内容**

- 不要再问为什么 `资源文件` 的 `plate` 和 `rating` 文件夹是空的或缺少文件
- 不要再问为什么 `资源文件` 的 `plate` 和 `rating` 文件夹是空的或缺少文件
- 不要再问为什么 `资源文件` 的 `plate` 和 `rating` 文件夹是空的或缺少文件

## 安装


1. 安装 `nonebot-plugin-maimaidx`

    - 使用 `nb-cli` 安装
        ``` python
        nb plugin install nonebot-plugin-maimaidx
        ```
    - 使用 `pip` 安装
        ``` python
        pip install nonebot-plugin-maimaidx
        ```
    - 使用源代码（不推荐） **需自行安装额外依赖**
        ``` git
        git clone https://github.com/Yuri-YuzuChaN/nonebot-plugin-maimaidx
        ```
    
2. 安装 `chromium`，**相关依赖已安装，请直接使用该指令执行**

    ``` shell
    playwright install --with-deps chromium
    ```

3. 安装 `微软雅黑` 字体，解决使用 `ginfo` 指令字体不渲染的问题，例如 `ubuntu`：`apt install fonts-wqy-microhei`


## 配置
   
1. 下载静态资源文件，将该压缩文件解压，且解压完为文件夹 `static`

   - [Cloudreve私人云盘](https://cloud.yuzuchan.moe/f/nXt6/Resource.7z)
   - [onedrive](https://yuzuai-my.sharepoint.com/:u:/g/personal/yuzu_yuzuchan_moe/IQDRlCkUqPlaR409p6Gv_RK6AZ-dJaBtbgZoJMvFI4IJiYw?e=eu5f2h)

2. 在 `.env` 文件中配置静态文件绝对路径 `MAIMAIDXPATH`

    ``` dotenv
    MAIMAIDXPATH=path.to.static

    # 例如 windows 平台，非 "管理员模式" 运行Bot尽量避免存放在C盘
    MAIMAIDXPATH=D:\bot\static
    # 例如 linux 平台
    MAIMAIDXPATH=/root/static
    ```

3. 可选，如果拥有 `diving-fish 查分器` 的开发者 `Token`，请在 `.env` 文件中配置 `MAIMAIDXTOKEN`
   
    ``` dotenv
    MAIMAIDXTOKEN=MAIMAITOKEN
    ```

4. 可选，如果你的服务器或主机不能顺利流畅的访问查分器和别名库的API，请在 `.env` 文件中配置代理。均为香港服务器代理中转，例如你的服务器访问查分器很困难，请设置 `MAIMAIDXPROBERPROXY` 为 `true`，别名库同理

    ``` dotenv
    # 查分器代理，推荐境外服务器使用
    MAIMAIDXPROBERPROXY=false
    # 别名代理，推荐国内服务器使用
    MAIMAIDXALIASPROXY=false
    ```

5. 可选，是否开启别名推送，不需要请在 `.env` 文件中配置 `MAIMAIDXALIASPUSH` 为 `false`，**注意，该配置为 `false` 时，将不会实时更新别名库，仅会在别名查歌或者跨日更新数据的时候才会更新别名库。如果群组的推送为开启状态，也不再进行推送，推送指令也一并失效**
   
    ``` dotenv
    MAIMAIDXALIASPUSH=false
    ```

6. 可选，是否将部分图片在保存在内存中，不需要请在 `.env` 文件中配置 `SAVEINMEM` 为 `false`

    ``` dotenv
    SAVEINMEM=false
    ```

> [!NOTE]
> 安装完插件需要使用定数表或完成表指令时，需私聊Bot使用 `更新定数表` 和 `更新完成表` 进行生成

> [!NOTE]
> 插件带有别名更新推送功能，默认开启全部群组推送，不需要请私聊Bot使用 `全局关闭别名推送` 指令关闭所有群组推送。群组需要单独使用请使用指令 `开启别名推送`。

## 指令

![img](https://raw.githubusercontent.com/Yuri-YuzuChaN/nonebot-plugin-maimaidx/master/nonebot_plugin_maimaidx/maimaidxhelp.png)