semvec/pypi-packages huggingface dataset:
ailabtools 0.0.64
Common tools for Zalo AILab

# Zalo AI Lab tools

[![PyPI version fury.io](https://badge.fury.io/py/ailabtools.svg)](https://pypi.python.org/pypi/ailabtools/)

[![MIT license](https://img.shields.io/badge/License-MIT-blue.svg)](https://lbesson.mit-license.org/)

[![PyPI pyversions](https://img.shields.io/pypi/pyversions/ailabtools.svg)](https://pypi.python.org/pypi/ailabtools/)

Pip package tools for Deep learning task

## Installation

```
pip install ailabtools
```

## Develop instruction

### Installation for developing

1. Clone project to folder `ailabtools`

2. Go to `ailabtools` folder, install using `pip`:

    ```
    pip install .
    ```

    for editable package (for development process), run:

    ```
    pip install --editable .
    ```

3. Check if package installed succesfully by running this python code:

    ```
    from ailabtools import common
    common.check()
    ```

    right output:

    ```
    >>> from ailabtools import common
    >>> common.check()
    AILab Server Check OK
    ```

    make sure that the output is `AILab Server Check OK` without any problems.

4. Optional, check information of package:
    ```
    pip show ailabtools
    ```

### Contribution process

- Deployment branch: `master`.

- Development branch: `develop`.

- Contribution process steps:
    1. Checkout new branch.
    2. Add own module.
    3. Create pull request to branch `develop`.
    4. Waiting for pull request review.
    5. Pull request merged, ready for beta deployment.
    6. Stable, ready for `master` merge for offical deployment.

### Package modified

Checkout `setup.py` for package information.

### Add module

Add module in `ailabtools` folder.

## Documents

Check wiki pages
dressuplite 0.1.2
Dependency-free lightweight fork of the package "dressup" to convert strings to use Unicode formatting
Requires Python: >=3.11
Keywords: unicode text

# dressuplite
**dressuplite** is a Python 3.11 package to convert strings to use Unicode formatting. It can for instance replace "words" into "𝔴𝔬𝔯𝔡𝔰", "🆆🅾🆁🅳🆂", and "𝔀𝓸𝓻𝓭𝓼". It is a lightweight fork of the original package [`dressup`](https://github.com/paw-lu/dressup/), but without any third-party runtime dependencies.

As a disclaimer, this package has no association with `dressup`.

[![cicd badge](https://github.com/impredicative/dressuplite/workflows/cicd/badge.svg?branch=master)](https://github.com/impredicative/dressuplite/actions?query=workflow%3Acicd+branch%3Amaster)


## Links
| Caption   | Link                                                  |
|-----------|-------------------------------------------------------|
| Repo      | https://github.com/impredicative/dressuplite/         |
| Changelog | https://github.com/impredicative/dressuplite/releases |
| Package   | https://pypi.org/project/dressuplite/                 |


## Development
For software development purposes only, the project can be set up on Ubuntu as below.
```bash
make setup-ppa
make install-py
make setup-venv
source ./venv/bin/activate
make install
make test
```

## Installation
Python ≥3.11 is required due to the use of [`tomllib`](https://docs.python.org/3/library/tomllib.html). This is due ot the use of TOML in the original package.

To install, run:

    $ pip install dressuplite

## Usage
To convert characters:
```python
>>> import dressuplite

>>> dressuplite.convert("Hello", unicode_type="negative circle")
'🅗🅔🅛🅛🅞'
```

To show all possible conversions:
```python
>>> import dressuplite

>>> for unicode_type, text in dressuplite.show_all("Hello").items():
...     print(f'{unicode_type.lower()}: {text}')
... 
circle: Ⓗⓔⓛⓛⓞ
negative circle: 🅗🅔🅛🅛🅞
monospace: Ｈｅｌｌｏ
math bold: 𝐇𝐞𝐥𝐥𝐨
math bold fraktur: 𝕳𝖊𝖑𝖑𝖔
math bold italic: 𝑯𝒆𝒍𝒍𝒐
math bold script: 𝓗𝓮𝓵𝓵𝓸
math double struck: ℍ𝕖𝕝𝕝𝕠
math monospace: 𝙷𝚎𝚕𝚕𝚘
math sans: 𝖧𝖾𝗅𝗅𝗈
math sans bold: 𝗛𝗲𝗹𝗹𝗼
math sans bold italic: 𝙃𝙚𝙡𝙡𝙤
math sans italic: 𝘏𝘦𝘭𝘭𝘰
parenthesized: ⒣⒠⒧⒧⒪
square: 🄷🄴🄻🄻🄾
negative square: 🅷🅴🅻🅻🅾
cute: Héĺĺő
math fraktur: ℌ𝔢𝔩𝔩𝔬
rock dots: Ḧëḷḷö
small caps: ʜᴇʟʟᴏ
stroked: Ħɇłłø
subscript: ₕₑₗₗₒ
superscript: ᴴᵉˡˡᵒ
inverted: ɥǝןןo
reversed: Hɘ⅃⅃o
```

Character mappings are precomputed and defined in [`translator.toml`](dressuplite/translator.toml).
asunder 0.1.0
CLI for refactoring python projects
Requires Python: >=3.10,<4.0

[![asunder](https://github.com/lalmei/asunder/actions/workflows/main.yml/badge.svg?branch=main)](https://github.com/lalmei/asunder/actions/workflows/main.yml) [![Maintainability](https://api.codeclimate.com/v1/badges/67d0f7c0715d81bef5f9/maintainability)](https://codeclimate.com/github/lalmei/asunder/maintainability) [![Test Coverage](https://api.codeclimate.com/v1/badges/67d0f7c0715d81bef5f9/test_coverage)](https://codeclimate.com/github/lalmei/asunder/test_coverage)
er-evaluation 1.0.2
An end-to-end evaluation framework for entity resolution systems.

[![Python package](https://github.com/OlivierBinette/er-evaluation/actions/workflows/python-package.yml/badge.svg)](https://github.com/OlivierBinette/er-evaluation/actions/workflows/python-package.yml)
[![PyPI version](https://badge.fury.io/py/er-evaluation.svg)](https://badge.fury.io/py/er-evaluation)

# 🔎 ER-Evaluation: An End-to-End Evaluation Framework for Entity Resolution Systems

**ER-Evaluation** is a Python package for the evaluation of entity resolution (ER) systems. It provides data structure definitions, summary statistics, visualizations, error analysis tools, and statistically principled performance estimators.

## Installation

Install the released version from PyPI using:
```bash
pip install er-evaluation
```

## Documentation

Please refer to the documentation website [olivierbinette.ca/er-evaluation](http://olivierbinette.ca/er-evaluation/build/html/index.html).

## Acknowledgements

**ER-Evaluation** is an extension of the [PatentsView/PatentsView-Evaluation](https://github.com/PatentsView/PatentsView-Evaluation/) project sponsored by the American Institutes for Research and the U.S. Patents and Trademarks Office. We aim to provide a unified source of evaluation tools for entity resolution systems which are maintained as an open source academic project.

### Publications

- [Binette, Olivier, Sokhna A York, Emma Hickerson, Youngsoo Baek, Sarvo Madhavan, Christina Jones. (2022). Estimating the Performance of Entity Resolution Algorithms: Lessons Learned Through PatentsView.org. arXiv e-prints: arxiv:2210.01230](https://arxiv.org/abs/2210.01230)

- Upcoming: "A Statistical Evaluation Framework for Black-Box Entity Resolution Systems With Application to Inventor Name Disambiguation"

### Funding

This project was made possible through support from the Natural Sciences and Engineering Research Council of Canada, Fonds de Recherche du Québec - Nature et Technologies, Duke University, the American Institutes for Research, and the U.S. Patents and Trademarks Office.

## Citation

Please acknowledge the above publications as well as the ER-Evaluation Python package:

- Binette, Olivier. (2022). ER-Evaluation: An End-to-End Evaluation Framework for Entity Resolution Systems. Available online at https://github.com/OlivierBinette/ER-Evaluation