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
Non Offensive(0) | Offensive (1) | |
---|---|---|
Train | 25625 | 6131 |
Test | 2812 | 716 |
Preprocessing Steps
Process | Description |
---|---|
Accented character transformation | Converting accented characters to their unaccented equivalents |
Lowercase transformation | Converting all text to lowercase |
Removing @user mentions | Removing @user formatted user mentions from text |
Removing hashtag expressions | Removing #hashtag formatted expressions from text |
Removing URLs | Removing URLs from text |
Removing punctuation and punctuated emojis | Removing punctuation marks and emojis presented with punctuation from text |
Removing emojis | Removing emojis from text |
Deasciification | Converting ASCII text into text containing Turkish characters |
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
Class | Precision | Recall | F1-score | Accuracy |
---|---|---|---|---|
Class 0 | 0.92 | 0.94 | 0.93 | 0.89 |
Class 1 | 0.73 | 0.67 | 0.70 | |
Macro | 0.83 | 0.80 | 0.81 |
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