Update README.md

README.md

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----
-license: cc-by-4.0
----
+---
+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 [[email protected]].