badd 1.0.32

In English

BadD(etector)

Description (How it can help)

BadD(etector) was created for detecting bad things in user-generated content in Russian. Now this library supports obscene words detection, advertising detection and toxicity detection. All the magic done by neural networks.

Requirements

1. Python 3.7+
2. PyTorch 1.8.1
3. Gensim 3.8.1
4. NLTK 3.2.5
5. pymorphy2 0.9.1
6. emoji 1.2.0

How to install

locally (dev mode)

python3 -m pip install -e <path-to-lib>

from github

pip install git+https://github.com/wksmirnowa/badd.git@master

from pip

pip install badd

Usage

Download files and models for:

• ObsceneDetector
• AdDetector
• ToxicDetector

Obscene words detection

Import the ObsceneDetector class

import torch
from badd import ObsceneDetector

Set pathes to files and device

# path to vocab
vocab_path = "obscene_vocab.json"
# path to embeddings
fasttext_path = "obscene_embeddings.pickle"
# path to model 
model_path = "obscene_model_cpu.pth"
# set device
device = torch.device('cpu')

Use ObsceneDetector

obscene_detector = ObsceneDetector(vocab_path, fasttext_path, model_path, device)

Predict every word in text

obscene_detector.predict_text(text)

Predict probability for every word in text

obscene_detector.predict_probability(text)

Check whether any obscene word is in text

obscene_detector.obscene_in_text(text)

Attributes

• obscene_detector.obscene_words list of found obscene words. Available after one of the methods (predict_probability, predict_text, obscene_in_text) was runned.

Ad detection

Import the AdDetector class

import torch
from badd import AdDetector

Set pathes to files and device

# path to vocab
vocab_path = "ad_vocab.json"
# path to embeddings
fasttext_path = "ad_embeddings.pickle"
# path to model 
model_path = "ad_model_cpu.pth"
# set device
device = torch.device('cpu')

Use AdDetector

ad_detector = AdDetector(vocab_path, fasttext_path, model_path, device)

Predict text

ad_detector.predict_text(text)

Predict probability for text

ad_detector.predict_probability(text)

Check whether a text is ad

ad_detector.is_ad(text)

Toxic texts detection

Import the ToxicDetector class

import torch
from badd import ToxicDetector

Set pathes to files and device

# path to vocab
vocab_path = "toxic_vocab.json"
# path to embeddings
fasttext_path = "toxic_embeddings.pickle"
# path to model 
model_path = "toxic_model_cpu.pth"
# set device
device = torch.device('cpu')

Use AdDetector

toxic_detector = ToxicDetector(vocab_path, fasttext_path, model_path, device)

Predict text

toxic_detector.predict_text(text)

Predict probability for text

toxic_detector.predict_probability(text)

Check whether a text is toxic

toxic_detector.is_toxic(text)