Research and academic papers in Natural Language Processing.
This research explores the application of transformer-based models like mBERT and XLM-RoBERTa for detecting offensive content in Ethiopian languages, addressing the challenges of limited annotated datasets.
View PublicationA comparative analysis of zero-shot and few-shot learning techniques for sentiment classification across diverse linguistic structures.
View PublicationThis research explores the application of transformer-based models like mBERT and XLM-RoBERTa for detecting offensive content in Ethiopian languages, addressing the challenges of limited annotated datasets.
View PublicationThis research explores the application of transformer-based models like mBERT and XLM-RoBERTa for detecting offensive content in Ethiopian languages, addressing the challenges of limited annotated datasets.
View PublicationThis research explores the application of transformer-based models like mBERT and XLM-RoBERTa for detecting offensive content in Ethiopian languages, addressing the challenges of limited annotated datasets.
View PublicationThis research explores the application of transformer-based models like mBERT and XLM-RoBERTa for detecting offensive content in Ethiopian languages, addressing the challenges of limited annotated datasets.
View PublicationThis research explores the application of transformer-based models like mBERT and XLM-RoBERTa for detecting offensive content in Ethiopian languages, addressing the challenges of limited annotated datasets.
View PublicationThis research explores the application of transformer-based models like mBERT and XLM-RoBERTa for detecting offensive content in Ethiopian languages, addressing the challenges of limited annotated datasets.
View Publication