EasyModerationKit is a public transparency statement. It declares any repositories and legalities used in the EasyModeration system. It allows for implementing EasyModeration into an advanced character/word/phrase detection system.

EasyModerationKit Dependencies

• PaddleOCR
• TensorFlowTTS
• scikit-fuzzy
• Detoxify

pip install "paddleocr>=2.0.1" # Recommend to use version 2.0.1+

• For windows users: If you getting this error OSError: [WinError 126] The specified module could not be found when you install shapely on windows. Please try to download Shapely whl file here.

  Reference: Solve shapely installation on windows

• For layout analysis users, run the following command to install Layout-Parser

  pip3 install -U https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl

$ pip install TensorFlowTTS

Scikit-Fuzzy depends on

• NumPy >= 1.6
• SciPy >= 0.9
• NetworkX >= 1.9

and is available on PyPi! The latest stable release can always be obtained and installed simply by running

$ pip install -U scikit-fuzzy

which will also work to upgrade existing installations to the latest release.

If you prefer to install from source or develop this package, you can fork and clone this repository then install SciKit-Fuzzy by running

$ python setup.py install

or develop locally by running

$ python setup.py develop

If you prefer, you can use SciKit-Fuzzy without installing by simply exporting this path to your PYTHONPATH variable.

###Installation

The multilingual model has been trained on 7 different languages so it should only be tested on: english, french, spanish, italian, portuguese, turkish or russian.

# install detoxify  

pip install detoxify

from detoxify import Detoxify

# each model takes in either a string or a list of strings

results = Detoxify('original').predict('example text')

results = Detoxify('unbiased').predict(['example text 1','example text 2'])

results = Detoxify('multilingual').predict(['example text','exemple de texte','texto de ejemplo','testo di esempio','texto de exemplo','örnek metin','пример текста'])

# to specify the device the model will be allocated on (defaults to cpu), accepts any torch.device input

model = Detoxify('original', device='cuda')

# optional to display results nicely (will need to pip install pandas)

import pandas as pd

print(pd.DataFrame(results, index=input_text).round(5))

Read LICENSE.txt for more information.

Created with ❤️ by Aarav-USA