---
language: az
license: cc-by-4.0
library_name: transformers
tags:
- text-classification
- toxicity
- azerbaijani
- multi-label-classification
pipeline_tag: text-classification
datasets:
- LocalDoc/toxic_dataset_classification_azerbaijani
base_model:
- microsoft/mdeberta-v3-base
---

# Azerbaijani Toxicity Classifier

This is a multi-label text classification model fine-tuned to detect various types of toxicity in **Azerbaijani** text.

The model is based on `mDeBERTa-v3` and can identify the following categories:
- `toxicity` (Overall Toxicity)
- `severe_toxicity`
- `obscene`
- `threat`
- `insult`
- `identity_attack`
- `sexual_explicit`

## Model Description

This model is designed for content moderation and analysis of online communication in the Azerbaijani language. It takes a string of text as input and returns a probability score for each of the seven toxicity categories. This allows for nuanced moderation, distinguishing between general insults, threats, or sexually explicit content.

## How to Use

You can use this model directly with the `transformers` library.

First, make sure you have the necessary libraries installed:
```bash
pip install transformers torch
```

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

def classify_toxicity():
    text_to_classify = "Sən nə yaramaz adamsan"
    
    model_id = "LocalDoc/azerbaijani_toxicity_classifier"
    
    # The order of labels must match the model's output
    label_names = [
        'identity_attack',
        'insult',
        'obscene',
        'severe_toxicity',
        'sexual_explicit',
        'threat',
        'toxicity'
    ]
    
    print(f"Loading model: {model_id}...")
    try:
        tokenizer = AutoTokenizer.from_pretrained(model_id)
        model = AutoModelForSequenceClassification.from_pretrained(model_id)
    except Exception as e:
        print(f"Error loading model. Make sure the repository '{model_id}' is public and contains the model files.")
        print(f"Details: {e}")
        return

    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    model.to(device)
    model.eval()
    print(f"Model loaded successfully on {device}.")
-
    
    inputs = tokenizer(
        text_to_classify,
        truncation=True,
        padding=True,
        return_tensors='pt'
    ).to(device)

    
    with torch.no_grad():
        outputs = model(**inputs)
        # Apply sigmoid to get probabilities for each category
        probabilities = torch.sigmoid(outputs.logits).cpu().numpy()[0]


    threshold = 0.5
    overall_toxicity_score = probabilities[-1] # The last label is 'toxicity'
    is_toxic = overall_toxicity_score > threshold

    print("\n" + "="*50)
    print("TOXICITY ANALYSIS RESULTS")
    print("="*50)
    print(f"Text: {text_to_classify}")

    status = "TOXIC" if is_toxic else "NOT TOXIC"
    print(f"\nOverall Status: {status} (Confidence: {overall_toxicity_score:.3f})")

    print("\nCategory Scores:")
    for i, category in enumerate(label_names):
        score = probabilities[i]
        formatted_name = category.replace('_', ' ').capitalize()
        print(f"  - {formatted_name:<20}: {score:.4f}")
        
    print("="*50)


if __name__ == "__main__":
    classify_toxicity()

```

```bash
==================================================
TOXICITY ANALYSIS RESULTS
==================================================
Text: Sən nə yaramaz adamsan

Overall Status: TOXIC (Confidence: 0.987)

Category Scores:
  - Identity attack     : 0.0004
  - Insult              : 0.9878
  - Obscene             : 0.0056
  - Severe toxicity     : 0.0002
  - Sexual explicit     : 0.0002
  - Threat              : 0.0006
  - Toxicity            : 0.9875
==================================================
```

## Intended Use & Limitations

This model is intended to be used as a tool for content moderation to help flag potentially harmful content for human review.

**Limitations:**
*   The model may struggle with sarcasm, irony, or other forms of nuanced language.
*   Its performance is dependent on the data it was trained on and may exhibit biases present in that data.
*   It should not be used to make fully automated, final decisions about content or users without a human-in-the-loop.

## Training

The model was fine-tuned on a private dataset of Azerbaijani text labeled for the seven toxicity categories mentioned above. It was trained as a multi-label classification task, where each text can belong to one, multiple, or no categories.

## License

This model is licensed under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** license. You are free to share and adapt the material for any purpose, even commercially, as long as you give appropriate credit. For more details, see the [license terms](https://creativecommons.org/licenses/by/4.0/).


## Contact

For more information, questions, or issues, please contact LocalDoc at [v.resad.89@gmail.com].