chore: set author ref

README.md

@@ -34,4 +34,10 @@ for t in predicted:
 The model was trained using two NVIDIA A100 GPUs on over 5.3 million examples from the "kz-transformers/multidomain-kazakh-dataset." We conducted training across 10 epochs, handling large batches of data efficiently through gradient accumulation. The learning setup included a slow build-up in the learning rate to maximize learning stability and was optimized over 208,100 steps, focusing on improving the model’s ability to understand and generate the Kazakh language.
 
 ## Limitations and Bias
-As with any language model, roberta-kaz-large may inherently learn biases present in the training data. Users should be cautious and evaluate the model in diverse contexts to ensure it performs as expected, especially in sensitive applications.
+As with any language model, roberta-kaz-large may inherently learn biases present in the training data. Users should be cautious and evaluate the model in diverse contexts to ensure it performs as expected, especially in sensitive applications.
+
+## Model Authors
+
+**Name:** Kadyrbek Nurgali
+- **Email:** [email protected]
+- **LinkedIn:** [Kadyrbek Nurgali](https://www.linkedin.com/in/nurgali-kadyrbek-504260231/)