Update README.md

Update Model Card

README.md

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-{}
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-
-Class 0: Insult
-Class 1: Other
-Class 2: PROFANITY
-Class 3: Racist
-Class 4: Sexist
-
-# Model Card for Toxicity Detection Model
-
-This model is a fine-tuned version of bert-base-uncased for toxicity detection in Turkish text. It has been trained on labeled datasets containing online comments categorized by their toxicity levels. The model uses the Hugging Face transformers library and is suitable for sequence classification tasks. This work was completed as a project assignment for the Natural Language Processing (CENG493) course at Çankaya University.
-
-- **Model Type:** Sequence Classification
-- **Language(s):** Turkish
-- **License:** GNU GENERAL PUBLIC LICENSE
-- **Fine-tuned from:** `dbmdz/bert-base-turkish-cased`
-
-## Uses
-
-This model can be used directly to analyze the toxicity of text in English. For example:
-
-- Content moderation in online forums and social media platforms
-- Filtering harmful language in customer reviews or feedback
-- Monitoring and preventing cyberbullying in messaging applications
-
-### Downstream Use
-
-- Integrating toxic language filtering into chatbots or virtual assistants
-- Using it as part of a sentiment analysis pipeline
-
-
-### Out-of-Scope Use
-
-- Not suitable for analyzing languages other than Turkish
-- Should not be used for sensitive decision-making without human oversight
-
-
-## Bias, Risks, and Limitations
-
-The model may inherit biases from the training data, including overrepresentation or underrepresentation of certain demographics or topics. It may also misclassify non-toxic content as toxic or fail to detect subtler forms of toxicity.
-
-### Recommendations
-
-Users should:
-
-- Avoid deploying the model in high-stakes scenarios without additional validation.
-- Regularly monitor performance and update the model if new biases are detected.
-
-### Training Data
-
-https://huggingface.co/datasets/Overfit-GM/turkish-toxic-language
-
-## Evaluation
-
+---
+license: gpl-3.0
+datasets:
+- Overfit-GM/turkish-toxic-language
+language:
+- tr
+base_model:
+- dbmdz/bert-base-turkish-cased
+pipeline_tag: text-classification
+---
+
+Class 0: Insult
+Class 1: Other
+Class 2: PROFANITY
+Class 3: Racist
+Class 4: Sexist
+
+# Model Card for Toxicity Detection Model
+
+This model is a fine-tuned version of bert-base-uncased for toxicity detection in Turkish text. It has been trained on labeled datasets containing online comments categorized by their toxicity levels. The model uses the Hugging Face transformers library and is suitable for sequence classification tasks. This work was completed as a project assignment for the Natural Language Processing (CENG493) course at Çankaya University.
+
+- **Model Type:** Sequence Classification
+- **Language(s):** Turkish
+- **License:** GNU GENERAL PUBLIC LICENSE
+- **Fine-tuned from:** `dbmdz/bert-base-turkish-cased`
+
+## Uses
+
+This model can be used directly to analyze the toxicity of text in English. For example:
+
+- Content moderation in online forums and social media platforms
+- Filtering harmful language in customer reviews or feedback
+- Monitoring and preventing cyberbullying in messaging applications
+
+### Downstream Use
+
+- Integrating toxic language filtering into chatbots or virtual assistants
+- Using it as part of a sentiment analysis pipeline
+
+
+### Out-of-Scope Use
+
+- Not suitable for analyzing languages other than Turkish
+- Should not be used for sensitive decision-making without human oversight
+
+
+## Bias, Risks, and Limitations
+
+The model may inherit biases from the training data, including overrepresentation or underrepresentation of certain demographics or topics. It may also misclassify non-toxic content as toxic or fail to detect subtler forms of toxicity.
+
+### Recommendations
+
+Users should:
+
+- Avoid deploying the model in high-stakes scenarios without additional validation.
+- Regularly monitor performance and update the model if new biases are detected.
+
+### Training Data
+
+https://huggingface.co/datasets/Overfit-GM/turkish-toxic-language
+
+## Evaluation
+
 The model was evaluated on a held-out test set containing a balanced mix of toxic and non-toxic examples.