semvec/pypi-packages huggingface dataset:
clickloader 0.2.0
Click Command Loader, permit to load Click command from a given folder.
Requires Python: >=3.7,<4.0

# Click Command Loader (CCL)

![Click Command Loader](.github/CCL.png)

Click Command Loader (Referred as CCL) is an additional package for [Click](https://click.palletsprojects.com/) to **load Click commands from a folder.**

As such, by having the following folder structure:

```ascii
my_project/
├─ commands/
│  ├─ create.py
│  ├─ delete.py
│  ├─ list.py
├─ __init__.py
```

The commands `create`, `delete` and `list` will be registered in your Click app!

## Installation

I'm a package that is available on [PyPi](https://pypi.org/project/clickloader)!

With Pip

```sh
pip install clickloader
```

With Poetry

```sh
poetry add clickloader
```

## Example

Let's consider you have the following commands structure:

```ascii
my_project/
├─ commands/
│  ├─ create.py
│  ├─ delete.py
│  ├─ list.py
├─ cli.py
```

Inside your `cli.py`, you should have your base logic for your CLI app.
Originally, this is where all your commands would be registered, but CCL will help you leverage the work here.

Here's is what the content of the `cli.py` would be:

```py
import click
import ccl
import pathlib


path_to_commands = pathlib.Path(__file__, "..", "commands")

my_cli = click.Group("MyCLI")
ccl.register_commands(my_cli, path_to_commands)

if __name__ == "__main__":
    my_cli()
```

And voilà! All commands inside your "commands" folder have been registered!

## Behind the scene

When registering commands, CCL will do the following:

1. List files and folder inside the given source.

2. Iterate over directory, if a file is found, go to step 3, if a folder is found, go to step 2.1

    2.1. If folder, see if a ``__init__.py`` exist, if it exist, attempt to export a function that contains a click decorator **(MUST BE A GROUP)**. If not found, create a click group, then continue to step 3 using the indicated group.

3. If file, attempt to export a function that contains a click decorator **(MUST BE A COMMAND)**, then add it to the given group.

4. Continue until all files/folder have been scanned.
smcore-beta 0.0.20
Package providing base sm agent classes and clients
Requires Python: >=3.10

# smcore

smcore provides base class agents and blackboard clients for use with [SimpleMind's go implementation](https://gitlab.com/sm-ai-team/sm-core-go).

This is still a work in progress and not yet ready for "primetime."  Functionality and API are likely to change until this message is removed.
UnBIASing 1.0
A package to detect and debias text using pretrained models

# UnBIASing

UnBIASing is a Python package that classifies, detects, and debiases textual content to promote unbiased information. By leveraging advanced machine learning models, UnBIASing provides users with tools to analyze and correct biases in their texts.

## Features

- **Bias Classification**: Classifies textual content based on its bias using state-of-the-art models.
  
- **Named Entity Recognition (NER)**: Detects named entities within the text that might be indicative of bias.
  
- **Text Debiasing**: Provides unbiased or debiased versions of the input text using an ensemble of advanced models.

## Installation

```bash
pip install UnBIASing
```

## Usage

Here's a basic example of how to use the `BiasPipeline` from UnBIASing:

```python
from unbias import BiasPipeline

pipeline = BiasPipeline()
texts = ["Your sample text goes here."]
classification_results, ner_results, debiaser_results = pipeline.process(texts)

# If you wish to print the results
pipeline.pretty_print(texts, classification_results, ner_results, debiaser_results)

# Convert results to a Pandas DataFrame
df = results_to_dataframe(texts, classification_results, ner_results, debiaser_results)
print(df)
```

## Dependencies

- Transformers
- Torch
- Pandas
- SentencePiece

## License

[MIT](https://opensource.org/licenses/MIT)

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We hope `UnBIASing` proves useful in your journey to make the digital world a more inclusive and unbiased space. For any queries or feedback, feel free to Shaina Raza at shaina.raza@utoronto.ca

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