bert-offensive-lang-detection-tr

Offensive Language Detection For Turkish Language

Model Description

This model has been fine-tuned using dbmdz/bert-base-turkish-128k-uncased model with the OffensEval 2020 dataset. The offenseval-tr dataset contains 31,756 annotated tweets.

Dataset Distribution

Preprocessing Steps

The performance of each pre-process was analyzed. Removing digits and keeping hashtags had no effect.

Usage

Install necessary libraries:

pip install git+https://github.com/emres/turkish-deasciifier.git

pip install keras_preprocessing

Pre-processing functions are below:

from turkish.deasciifier import Deasciifier
def deasciifier(text):
    deasciifier = Deasciifier(text)
    return deasciifier.convert_to_turkish()

def remove_circumflex(text):
    circumflex_map = {
        'â': 'a',
        'î': 'i',
        'û': 'u',
        'ô': 'o',
        'Â': 'A',
        'Î': 'I',
        'Û': 'U',
        'Ô': 'O'
    }

    return ''.join(circumflex_map.get(c, c) for c in text)    
def turkish_lower(text):
    turkish_map = {
        'I': 'ı',
        'İ': 'i',
        'Ç': 'ç',
        'Ş': 'ş',
        'Ğ': 'ğ',
        'Ü': 'ü',
        'Ö': 'ö'
    }
    return ''.join(turkish_map.get(c, c).lower() for c in text)

Clean text using below function:

import re

def clean_text(text):
    # Metindeki şapkalı harfleri kaldırma
    text = remove_circumflex(text)
    # Metni küçük harfe dönüştürme
    text = turkish_lower(text)
    # deasciifier
    text = deasciifier(text)
    # Kullanıcı adlarını kaldırma
    text = re.sub(r"@\S*", " ", text)
    # Hashtag'leri kaldırma
    text = re.sub(r'#\S+', ' ', text)
    # URL'leri kaldırma
    text = re.sub(r"http\S+|www\S+|https\S+", ' ', text, flags=re.MULTILINE)
    # Noktalama işaretlerini ve metin tabanlı emojileri kaldırma
    text = re.sub(r'[^\w\s]|(:\)|:\(|:D|:P|:o|:O|;\))', ' ', text)
    # Emojileri kaldırma
    emoji_pattern = re.compile("["
                           u"\U0001F600-\U0001F64F"  # emoticons
                           u"\U0001F300-\U0001F5FF"  # symbols & pictographs
                           u"\U0001F680-\U0001F6FF"  # transport & map symbols
                           u"\U0001F1E0-\U0001F1FF"  # flags (iOS)
                           u"\U00002702-\U000027B0"
                           u"\U000024C2-\U0001F251"
                           "]+", flags=re.UNICODE)
    text = emoji_pattern.sub(r' ', text)

    # Birden fazla boşluğu tek boşlukla değiştirme
    text = re.sub(r'\s+', ' ', text).strip()
    return text

Model Initialization

# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("TURKCELL/bert-offensive-lang-detection-tr")
model = AutoModelForSequenceClassification.from_pretrained("TURKCELL/bert-offensive-lang-detection-tr")

Check if sentence is offensive like below:

import numpy as np
def is_offensive(sentence):
    d = {
        0: 'non-offensive',
        1: 'offensive'
    }
    normalize_text = clean_text(sentence)
    test_sample = tokenizer([normalize_text], padding=True, truncation=True, max_length=256, return_tensors='pt')

    test_sample = {k: v.to(device) for k, v in test_sample.items()}

    output = model(**test_sample)
    y_pred = np.argmax(output.logits.detach().cpu().numpy(), axis=1)

    print(normalize_text, "-->", d[y_pred[0]])
    return y_pred[0]

is_offensive("@USER Mekanı cennet olsun, saygılar sayın avukatımız,iyi günler dilerim")
is_offensive("Bir Gün Gelecek Biriniz Bile Kalmayana Kadar Mücadeleye Devam Kökünüzü Kurutacağız !! #bebekkatilipkk")

Evaluation

Evaluation results on test set shown on table below. We achive %89 accuracy on test set.

Model Performance Metrics