What question did this study set out to answer?

To develop a model for detecting sexually explicit content in the Amharic language.

February 2, 2026PeerJ Computer ScienceOpen Access

An Amharic sexually explicit content detection model using fine-tuned bidirectional encoder representation from transformers and explainable artificial intelligence

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This model detects sexually explicit content in Amharic, suggesting a reliable tool for digital safety measures.

To develop a model for detecting sexually explicit content in the Amharic language.
Utilized a fine-tuned BERT model trained on previously annotated hate speech data.
Annotated a dataset of 34,710 comments collected from various social media platforms.
Applied Explainable AI techniques, specifically LIME, to interpret model decisions.
Achieved accuracy of 94%, precision of 95%, recall of 94%, and F1-score of 94%.
Revealed how the model classifies content by computing probabilities and highlighting significant words.
Outperformed multiple state-of-the-art text classification methods, including BERT, SVM, MLP, and CNN.

Demeke Endalie (2026) studied this question.

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February 2, 2026PeerJ Computer ScienceOpen Access

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Authors

This model detects sexually explicit content in Amharic, suggesting a reliable tool for digital safety measures.

To develop a model for detecting sexually explicit content in the Amharic language.
Utilized a fine-tuned BERT model trained on previously annotated hate speech data.
Annotated a dataset of 34,710 comments collected from various social media platforms.
Applied Explainable AI techniques, specifically LIME, to interpret model decisions.
Achieved accuracy of 94%, precision of 95%, recall of 94%, and F1-score of 94%.
Revealed how the model classifies content by computing probabilities and highlighting significant words.
Outperformed multiple state-of-the-art text classification methods, including BERT, SVM, MLP, and CNN.

Demeke Endalie (2026) studied this question.

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