ariyul/turkish-toxic-language-detection

language: tr tags:

• —toxicity
• —text-classification
• —turkish
• —transformers
• —bert license: mit datasets:
• —Overfit-GM/turkish-toxic-language metrics:
• —accuracy
• —f1
• —precision
• —recall model-index:
• —name: Turkish Toxic Language Detection Model results:
• —task: type: text-classification name: Text Classification dataset: name: Turkish Toxic Language Dataset type: Overfit-GM/turkish-toxic-language metrics:
• —name: Accuracy type: accuracy value: 0.96
• —name: F1 type: f1 value: 0.96
• —name: Precision type: precision value: 0.96
• —name: Recall type: recall value: 0.96 ---

🇹🇷 Turkish Toxic Language Detection Model 🧠🔥

This model is a fine-tuned version of `dbmdz/bert-base-turkish-cased` for binary toxicity classification in Turkish text. It was trained using a cleaned and preprocessed version of the `Overfit-GM/turkish-toxic-language` dataset.

📊 Performance

Confusion Matrix

🧪 Preprocessing Details (cleanedcorrectedtext)

The model is trained on the cleaned_corrected_text column, which is derived from corrected_text using basic regex-based cleaning steps and manual slang filtering. Here's how:

🔧 Cleaning Function

def clean_corrected_text(text):
    text = text.lower()
    text = re.sub(r"http\S+|www\S+|https\S+", '', text, flags=re.MULTILINE)  # URL removal
    text = re.sub(r"@\w+", '', text)  # remove @mentions
    text = re.sub(r"[^\w\s.,!?-]", '', text)  # remove special characters (e.g., emojis)
    text = re.sub(r"\s+", ' ', text).strip()  # normalize whitespaces
    return text

🧹 Manual Slang Filtering

slang_words = ["kanka", "lan", "knk", "bro", "la", "birader", "kanki"]

def remove_slang(text):
    for word in slang_words:
        text = text.replace(word, "")
    return text.strip()

✅ Applied Steps Summary

📌 Conclusion: cleaned_corrected_text is a lightly cleaned, non-linguistically processed text column. The model is trained directly on this.

💡 Example Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("fc63/turkish_toxic_language_detection_model")
model = AutoModelForSequenceClassification.from_pretrained("fc63/turkish_toxic_language_detection_model")

def predict_toxicity(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding="max_length", max_length=128)
    outputs = model(**inputs)
    predicted = torch.argmax(outputs.logits, dim=1).item()
    return "Toxic" if predicted == 1 else "Non-Toxic"

🛠 Training Details

• —Trainer: Hugging Face Trainer API
• —Epochs: 3
• —Batch size: 16
• —Learning Rate: 2e-5
• —Eval Strategy: Epoch-based
• —Undersampling: Applied to balance class distribution

📁 Dataset

Dataset used: `Overfit-GM/turkish-toxic-language` Final dataset size after preprocessing and balancing: 54068 samples