“The bidirectional transformer model BERT has been used for prediction because of its state-of-the-art efficiency over other Machine Learning(ML) models. The model-agnostic algorithm LIME generates explanations for the output of a trained classifier and predicts the features that influence the model’s decision. The predictions generated from the model were evaluated manually, and after thorough evaluation, we observed that the model performs efficiently in predicting and explaining its prediction. Lastly, we suggest further directions for the expansion of the provided research work.” https://recap.uni-trier.de/static/3bb18009cd27ce0999eef0fb60153d61/90.pdf Share this: Print (Opens in new window) Print Share on Facebook (Opens in new window) Facebook Share on LinkedIn (Opens in new window) LinkedIn Share on Reddit (Opens in new window) Reddit Share on WhatsApp (Opens in new window) WhatsApp Share on Bluesky (Opens in new window) Bluesky Email a link to a friend (Opens in new window) Email Like this:Like Loading... Post navigation Hate Speech and Offensive Language Detection using an Emotion-aware Shared Encoder (arXiv) Combating Hate Speech Online With AI (IEEE)