README.md

---
language:
- tr
tags:
- text-classification
- bert
- offensive-language-detection
- turkish
- boun-tabilab
datasets:
- offenseval-tr
metrics:
- accuracy
- f1
model-index:
- name: atahanuz/bert-offensive-classifier
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: OffensEval-2020-TR
      type: offenseval-tr
    metrics:
      - name: Accuracy
        type: accuracy
        value: 0.936
      - name: F1
        type: f1
        value: 0.912
base_model: boun-tabilab/TabiBERT
---

# Turkish Offensive Language Classifier (BERT)

This model is a fine-tuned version of [**boun-tabilab/TabiBERT**](https://huggingface.co/boun-tabilab/TabiBERT) trained on the **OffensEval-2020-TR** dataset. It is designed to perform binary classification to detect offensive language in Turkish text.

## 📊 Model Details

| Feature | Description |
| :--- | :--- |
| **Model Architecture** | BERT (Base Uncased Turkish - TabiBERT) |
| **Task** | Binary Text Classification (Offensive vs. Not Offensive) |
| **Language** | Turkish (tr) |
| **Dataset** | OffensEval 2020 (Turkish Subtask) |
| **Trained By** | atahanuz |

## 🚀 Usage

The easiest way to use this model is via the Hugging Face `pipeline`.

### Method 1: Using the Pipeline (Recommended)

```python
from transformers import pipeline

classifier = pipeline("text-classification", model="atahanuz/bert-offensive-classifier")

text = "Bu harika bir filmdi, çok beğendim."
result = classifier(text)[0]

label = "Offensive" if result['label'] == "LABEL_1" else "Not Offensive"

print(f"Prediction: {label} (Score: {result['score']:.4f})")

# Prediction: Not Offensive (Score: 1.0000)
```

### Method 2: Manual PyTorch Implementation

If you need more control over the tokens or logits, use the standard `AutoModel` approach:

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# 1. Load model and tokenizer
model_name = "atahanuz/bert-offensive-classifier"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# 2. Define label mapping
id2label = {0: "NOT", 1: "OFF"}

# 3. Tokenize and predict
text = "Bu harika bir filmdi, çok beğendim." # Example text
inputs = tokenizer(text, return_tensors="pt", truncation=True)

with torch.no_grad():
    logits = model(**inputs).logits

# 4. Get results
predicted_class_id = logits.argmax().item()
predicted_label = id2label[predicted_class_id]
confidence = torch.softmax(logits, dim=1)[0][predicted_class_id].item()

print(f"Text: {text}")
print(f"Prediction: {predicted_label} (Confidence: {confidence:.4f})")
```

## 🏷️ Label Mapping

The model outputs the following labels:

| Label ID | Label Name | Description |
| :--- | :--- | :--- |
| `0` | **NOT** | **Not Offensive** - Normal, non-hateful speech. |
| `1` | **OFF** | **Offensive** - Contains insults, threats, or inappropriate language. |

## 📝 Example Predictions

| Text | Label | Prediction |
| :--- | :--- | :--- |
| "Bu filmi çok beğendim, oyunculuklar harikaydı." | **NOT** | Non-Offensive |
| "Beynini kullanmayı denesen belki anlarsın." | **OFF** | Offensive |
| "Maalesef bu konuda sana katılamıyorum." | **NOT** | Non-Offensive |
| "Senin gibi aptal insanlar yüzünden bu haldeyiz." | **OFF** | Offensive |

## 📈 Performance

The model was evaluated on the test split of the OffensEval-2020-TR dataset (approx. 3,500 samples).

- **Accuracy:** `93.6%`
- **F1 Score:** `91.2%`

### Dataset Statistics
- **Training Samples:** 31,277
- **Test Samples:** 3,529

## ⚠️ Limitations and Bias

* **Context Sensitivity:** Like many BERT models, this classifier may struggle with sarcasm or offensive language that depends heavily on context not present in the input sentence.
* **Dataset Bias:** The model is trained on social media data (OffensEval). It may reflect biases present in that specific dataset or struggle with formal/archaic Turkish.
* **False Positives:** Certain colloquialisms or "tough love" expressions might be misclassified as offensive.

## 📚 Citation

If you use this model, please cite the TabiLAB model and OffensEval paper:

```bibtex
@misc{Türker2025Tabibert,
      title={TabiBERT: A Large-Scale ModernBERT Foundation Model and Unified Benchmarking Framework for Turkish}, 
      author={Melikşah Türker and Asude Ebrar Kızıloğlu and Onur Güngör and Susan Üsküdarlı},
      year={2025},
      eprint={2512.23065},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2512.23065}, 
}
```

```bibtex
@inproceedings{zampieri-etal-2020-semeval,
    title = "{SemEval}-2020 Task 12: Multilingual Offensive Language Identification in Social Media ({OffensEval} 2020)",
    author = "Zampieri, Marcos  and
      Nakov, Preslav  and
      Rosenthal, Sara  and
      Atanasova, Pepa  and
      Karadzhov, Georgi  and
      Mubarak, Hamdy  and
      Derczynski, Leon  and
      Pym, Z",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    year = "2020",
    publisher = "International Committee for Computational Linguistics",
}
```