Yes, the insights from this work can be applied to develop efficient encoder models for other language groups beyond South Slavic languages. Some key ways to apply these insights include:
Multilingual Pretraining: Following the approach of additional pretraining on a diverse set of languages, developers can apply similar strategies to create efficient encoder models for other language groups. By incorporating data from multiple languages during pretraining, the models can gain a broader understanding of language nuances.
Regular Evaluation: Implementing a system for regular evaluation of model performance during pretraining can help in identifying optimal training points and preventing performance degradation. This approach can be applied to ensure the model maintains its multilingual capabilities.
Hyperparameter Optimization: Fine-tuning hyperparameters based on the specific characteristics of the target language group can enhance the model's performance and efficiency. Adjusting parameters such as learning rate, batch size, and training steps can optimize the model for diverse language datasets.
Data Balancing: Ensuring a balanced mix of data from different languages within the target language group can help in maintaining the multilingual aspect of the model. By incorporating diverse language data, the model can develop a comprehensive understanding of various languages.
Task-Specific Fine-Tuning: Periodic fine-tuning of the model on task-specific datasets related to the target language group can reinforce language-specific knowledge while retaining multilingual capabilities. This approach can enhance the model's performance on specific tasks within the language group.