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--- |
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language: az |
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license: cc-by-4.0 |
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library_name: transformers |
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tags: |
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- text-classification |
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- toxicity |
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- azerbaijani |
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- multi-label-classification |
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pipeline_tag: text-classification |
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datasets: |
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- LocalDoc/toxic_dataset_classification_azerbaijani |
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base_model: |
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- microsoft/mdeberta-v3-base |
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--- |
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# Azerbaijani Toxicity Classifier |
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This is a multi-label text classification model fine-tuned to detect various types of toxicity in **Azerbaijani** text. |
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The model is based on `mDeBERTa-v3` and can identify the following categories: |
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- `toxicity` (Overall Toxicity) |
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- `severe_toxicity` |
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- `obscene` |
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- `threat` |
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- `insult` |
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- `identity_attack` |
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- `sexual_explicit` |
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## Model Description |
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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. |
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## How to Use |
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You can use this model directly with the `transformers` library. |
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First, make sure you have the necessary libraries installed: |
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```bash |
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pip install transformers torch |
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``` |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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def classify_toxicity(): |
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text_to_classify = "Sən nə yaramaz adamsan" |
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model_id = "LocalDoc/azerbaijani_toxicity_classifier" |
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# The order of labels must match the model's output |
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label_names = [ |
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'identity_attack', |
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'insult', |
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'obscene', |
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'severe_toxicity', |
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'sexual_explicit', |
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'threat', |
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'toxicity' |
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] |
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print(f"Loading model: {model_id}...") |
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try: |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForSequenceClassification.from_pretrained(model_id) |
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except Exception as e: |
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print(f"Error loading model. Make sure the repository '{model_id}' is public and contains the model files.") |
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print(f"Details: {e}") |
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return |
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
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model.to(device) |
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model.eval() |
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print(f"Model loaded successfully on {device}.") |
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- |
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inputs = tokenizer( |
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text_to_classify, |
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truncation=True, |
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padding=True, |
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return_tensors='pt' |
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).to(device) |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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# Apply sigmoid to get probabilities for each category |
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probabilities = torch.sigmoid(outputs.logits).cpu().numpy()[0] |
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threshold = 0.5 |
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overall_toxicity_score = probabilities[-1] # The last label is 'toxicity' |
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is_toxic = overall_toxicity_score > threshold |
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print("\n" + "="*50) |
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print("TOXICITY ANALYSIS RESULTS") |
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print("="*50) |
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print(f"Text: {text_to_classify}") |
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status = "TOXIC" if is_toxic else "NOT TOXIC" |
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print(f"\nOverall Status: {status} (Confidence: {overall_toxicity_score:.3f})") |
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print("\nCategory Scores:") |
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for i, category in enumerate(label_names): |
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score = probabilities[i] |
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formatted_name = category.replace('_', ' ').capitalize() |
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print(f" - {formatted_name:<20}: {score:.4f}") |
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print("="*50) |
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if __name__ == "__main__": |
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classify_toxicity() |
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``` |
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```bash |
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================================================== |
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TOXICITY ANALYSIS RESULTS |
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================================================== |
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Text: Sən nə yaramaz adamsan |
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Overall Status: TOXIC (Confidence: 0.987) |
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Category Scores: |
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- Identity attack : 0.0004 |
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- Insult : 0.9878 |
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- Obscene : 0.0056 |
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- Severe toxicity : 0.0002 |
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- Sexual explicit : 0.0002 |
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- Threat : 0.0006 |
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- Toxicity : 0.9875 |
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================================================== |
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``` |
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## Intended Use & Limitations |
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This model is intended to be used as a tool for content moderation to help flag potentially harmful content for human review. |
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**Limitations:** |
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* The model may struggle with sarcasm, irony, or other forms of nuanced language. |
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* Its performance is dependent on the data it was trained on and may exhibit biases present in that data. |
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* It should not be used to make fully automated, final decisions about content or users without a human-in-the-loop. |
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## Training |
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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. |
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## License |
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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/). |
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## Contact |
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For more information, questions, or issues, please contact LocalDoc at [[email protected]]. |
