---
language: tr
tags:
- bert
- turkish
- text-classification
- offensive-language-detection
license: mit
datasets:
- offenseval2020_tr
metrics:
- accuracy
- f1
- precision
- recall
---

 Offensive Language Detection For Turkish Language

## Model Description
This model has been fine-tuned using [dbmdz/bert-base-turkish-128k-uncased](https://huggingface.co./dbmdz/bert-base-turkish-128k-uncased) model with the [OffensEval 2020](https://huggingface.co./datasets/offenseval2020_tr) 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:

```python

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:

```python
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

```python
# 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:

```python
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]

```

```python
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     |          |