semvec/pypi-packages huggingface dataset:
attrbench 0.0.2
A benchmark for feature attribution techniques
Requires Python: >=3.6

# AttrBench: Metrics for Feature Attribution Techniques
This repository contains the source code for the `attrbench` package, which provides a [Pytorch]()-based implementation of several metrics for the evaluation of feature attribution maps and methods. The package is currently mainly focused on image data, but we plan to generalize the code to work for any type of data.

## Installation
To install the project, run:
```bash
pip install attrbench
```

## Available metrics
- Deletion
- Insertion
- IROF
- IIOF
- Impact Coverage
- Impact Score
- Infidelity
- Max-Sensitivity
- Minimal Subset
- Sensitivity-n
- Seg-Sensitivity-n

## Future work
We are mainly working in 3 general directions for the near future:
- Generalization of the benchmark to other data modalities than images.
- Optimization of GPU usage.
- Inclusion of new metrics
geeksforgeeks-golang-zh 2021.12.21.0
GeeksForGeeks Golang 中文教程
Requires Python: >=3.6

# GeeksForGeeks Golang 中文教程

> 原文：[GeeksForGeeks](https://geeksforgeeks.org/)
> 
> 协议：[CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/)
> 
> 阶段：机翻（1）
> 
> 欢迎任何人参与和完善：一个人可以走的很快，但是一群人却可以走的更远。

* [在线阅读](https://g4g-go.apachecn.org)
* [在线阅读（Gitee）](https://apachecn.gitee.io/doc-template/)
* [ApacheCN 学习资源](http://docs.apachecn.org/)

## 贡献指南

本项目需要校对，欢迎大家提交 Pull Request。

> 请您勇敢地去翻译和改进翻译。虽然我们追求卓越，但我们并不要求您做到十全十美，因此请不要担心因为翻译上犯错——在大部分情况下，我们的服务器已经记录所有的翻译，因此您不必担心会因为您的失误遭到无法挽回的破坏。（改编自维基百科）

## 联系方式

### 负责人

* [飞龙](https://github.com/wizardforcel): 562826179

### 其他

*   在我们的 [apachecn/geeksforgeeks-golang-zh](https://github.com/apachecn/geeksforgeeks-golang-zh) github 上提 issue.
*   发邮件到 Email: `apachecn@163.com`.
*   在我们的 [组织学习交流群](https://www.apachecn.org/#/docs/join) 中联系群主/管理员即可.

## 下载

### Docker

```
docker pull apachecn0/geeksforgeeks-golang-zh
docker run -tid -p <port>:80 apachecn0/geeksforgeeks-golang-zh
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install geeksforgeeks-golang-zh
geeksforgeeks-golang-zh <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g geeksforgeeks-golang-zh
geeksforgeeks-golang-zh <port>
# 访问 http://localhost:{port} 查看文档
```

## 赞助我们

![](http://data.apachecn.org/img/about/donate.jpg)
pythontrr 1.0.1
python-trr is a terminal app written in Python for typing practice.
Requires Python: >=3.7

# python-trr
Python-trr is an terminal app for typing practice. It is written in Python with one of its standard libraries - Curses, which means no additional packages are required to run this app as long as you have python3 installed in your environment. Python-trr is inspired by ( or a remake of ) [emacs-trr](https://github.com/kawabata/emacs-trr) on GNU Emacs.

## What does it do?
<img src="https://github.com/kaiwinut/python-trr/blob/main/docs/sample.gif" alt="sample video of python-trr" width="500"/>

The current version of python-trr ( as in Dec 2021 ) supports the following features:

- [x] Has a stable graphics interface in a standard 80 x 24 terminal window
- [x] Highlights correct / incorrect inputs
- [x] Shows time, error rate, kpm ( keystrokes per minute ) in real-time
- [x] Stores results and histories in local files
- [x] Presents recent game results in scatter plot style
- [x] Provides a settings menu ( accessible with the TAB key ) for user settings

## Usage
First, install python-trr with pip. 

`pip install pythontrr`

Then, from the terminal, simply run

`python3 -m pythontrr`

and start typing!

## Configuration

#### Via settings menu
Press TAB during the game to pause and enter settings mode. There are currently 5 settings and several options for users to choose from. Here are some keys you will find useful:

`Up / Down arrow key`

Navigate through different settings

`Left / Right arrow key`

Navigate through different options

`TAB`

Save changes and let the game resume

`ESC`

**Save your changes beforehand, otherwise your current game status and changes to settings will be lost**. 

Press ESC to exit the game. After restarting the game, you will find the new settings applied.

---

#### Via connfiguration file ( not recommended )
In `config.py` you can tweak the configuration as you like. Here are some tips:

`READ_RESULT_FROM_HISTORY`, `READ_HISTORY`

If set to True, then the program will look for the stored history in your local files. If there is not one, it will be created. If set to False, it erases all existing histories stored in the file and overwrites them.

`HISTORY_ENTRIES`

Specifies how many entries will be shown in the History block. The maximum is 3.

`WORD_COUNT`

Specifies how many words ( or in fact keystrokes ) will be shown in the Text block. The maximum is 350.

## References
Sample texts are scraped from [Project Gutenberg's Website](https://www.gutenberg.org/).
rail-label 0.1.2
Perspective label tool for railways
Keywords: python,video,railway,rail,train,labeling


<div align="center">
<img src="images/RailLabel.png">
</div>

## Installation
Label tool can be installed in multiple ways.
The preferred way ist to install as python apckage.

### Install as python package (recommended)
You may install this package system-wide, user-wide or in a virtual environment.
0. If necessary, activate the environment
```commandline
source activate <environment name>/bin/activate
```
1. Install the package`rail-label`. User wide installation with the `--user` flag  
```commandline
git+https://github.com/FloHofstetter/labeltool.git
```
Optionally via ssh:
```commandline
pip install git+ssh://git@github.com/FloHofstetter/labeltool.git
```

### Optional: Clone and launch manually
1. Clone this repository
```commandline
https://github.com/FloHofstetter/labeltool.git
git clone 
```
Optionally via ssh:
```commandline
git@github.com:FloHofstetter/labeltool.git
```
2. Create virtual environment and activate
```commandline
python3 -m venv venv
```
3. Install required packages
```commandline
pip install -r requirements.txt
```

## Application
1. Change in the dataset directory
```commandline
cd ~/dataset
```
2. Start labeltool
```commandline
python3 -m rail_label
```

Optional start with dataset path as CLI argument:
```commandline
python3 -m rail_label --data_path <dataset path>
```