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README.md
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## Usage
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## ⚠️ Limitations
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- **Biases:** The model may show biases present in the training dataset. Efforts were made to clean and deduplicate the corpus, but further bias mitigation might be necessary for sensitive applications.
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- **Domain Specificity:** While the dataset covers diverse domains, performance may vary for niche or underrepresented topics. For example, the dataset is heavily skewed toward 'news'-themed texts, while domains such as 'science' or 'medicine' are less represented.
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## Usage
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## 📬 Contact
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For inquiries, feedback, or contributions, please feel free to reach out to the core team:
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- [Stefan Krsteski](https://www.linkedin.com/in/stefan-krsteski-136abb235/) [📧](mailto:[email protected])
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- [Matea Tashkovska](https://www.linkedin.com/in/matea-tashkovska-774603198/) [📧](mailto:[email protected])
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- [Borjan Sazdov](https://www.linkedin.com/in/borjan-sazdov-4b2187211/) [📧](mailto:[email protected])
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## ⚠️ Limitations
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- **Biases:** The model may show biases present in the training dataset. Efforts were made to clean and deduplicate the corpus, but further bias mitigation might be necessary for sensitive applications.
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- **Domain Specificity:** While the dataset covers diverse domains, performance may vary for niche or underrepresented topics. For example, the dataset is heavily skewed toward 'news'-themed texts, while domains such as 'science' or 'medicine' are less represented.
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