sirunchained/bad-comment

Offensive Text Classifier for Persian Language

This repository contains an Offensive Text Classifier model trained to detect offensive language in Persian text. The model is built using scikit-learn and exported to ONNX format for efficient inference.

Model Description

This model classifies Persian text into two categories: Offensive (label 1) and Neutral (label 0). It uses a TfidfVectorizer for text feature extraction combined with a Support Vector Machine (SVM) classifier.

Dataset

The model was trained on the ParsOffensive dataset. This dataset consists of Persian comments labeled as either 'Offensive' or 'Neutral'.

Preprocessing

The text data underwent the following preprocessing steps:

• Normalization: Using hazm.Normalizer.
• Lemmatization: Using hazm.Lemmatizer.
• Stop-word Removal: Common Persian stop words were removed.
• Label Encoding: 'Neutral' and 'Offensive' labels were converted to numerical 0 and 1 respectively.
• Imbalance Handling: The ADASYN technique was applied to address class imbalance during training.

Model Architecture

The final pipeline consists of:

1. TfidfVectorizer: Converts raw text into a matrix of TF-IDF features.
2. SVC (Support Vector Classifier): A Support Vector Machine classifier with a radial basis function (RBF) kernel.

Performance

Below are the performance metrics on the test set:

              precision    recall  f1-score   support

           0       0.80      0.96      0.88      1043
           1       0.91      0.62      0.74       644

    accuracy                           0.83      1687
   macro avg       0.86      0.79      0.81      1687
weighted avg       0.84      0.83      0.82      1687

Detailed Metrics:

• Accuracy: 0.830
• Precision (Offensive): 0.909
• Recall (Offensive): 0.618
• F1-score (Offensive): 0.736

How to Use

Load the model

You can load the ONNX model and use it for inference. You will need to apply the same preprocessing steps as during training.

from hazm import Lemmatizer, Normalizer, stopwords_list

# recreate the preprocessing components (or load them if saved)
stopwords = stopwords_list()
lemmatizer = Lemmatizer()
normalizer = Normalizer()

def clean_sentences(sentence: str) -> str:
    return " ".join(lemmatizer.lemmatize(word) for word in normalizer.normalize(sentence).split(" ") if word not in stopwords)

Dependencies

• pandas
• numpy
• hazm
• scikit-learn
• imblearn
• skl2onnx
• onnxruntime
• huggingface_hub

Demo

You can try it here.