This model is designed for text classification tasks in the Kazakh language, based on the RoBERTa architecture and fine-tuned using the Small Kazakh Corpus dataset.
The model aims to enhance natural language processing (NLP) capabilities for the Kazakh language, particularly in text classification tasks.
Evaluation results show an improvement in both accuracy and F1-score:
Base model performance:
Accuracy: 50.30%
F1-score: 48.89%
Fine-tuned model performance:
Accuracy: 55.51% (+10%)
F1-score: 54.83% (+5%)
We will definitely add a bit later.
Tleubayeva Arailym, PhD student of Astana IT University
Tabuldin Aisultan, 3rd year student of Astana IT University
Aubakirov Sultan, 3rd year student of Astana IT University
This model is designed for text classification tasks in the Kazakh language, based on the RoBERTa architecture and fine-tuned using the Small Kazakh Corpus dataset.
The model aims to enhance natural language processing (NLP) capabilities for the Kazakh language, particularly in text classification tasks.
Evaluation results show an improvement in both accuracy and F1-score:
Base model performance:
Accuracy: 50.30%
F1-score: 48.89%
Fine-tuned model performance:
Accuracy: 55.51% (+10%)
F1-score: 54.83% (+5%)
We will definitely add a bit later.
Tleubayeva Arailym, PhD student of Astana IT University
Tabuldin Aisultan, 3rd year student of Astana IT University
Aubakirov Sultan, 3rd year student of Astana IT University