  Bias detection in text is crucial for combating the spread of negative
stereotypes, misinformation, and biased decision-making. Traditional language
models frequently face challenges in generalizing beyond their training data
and are typically designed for a single task, often focusing on bias detection
at the sentence level. To address this, we present the Contextualized
Bi-Directional Dual Transformer (CBDT) \textcolor{green}{\faLeaf} classifier.
This model combines two complementary transformer networks: the Context
Transformer and the Entity Transformer, with a focus on improving bias
detection capabilities. We have prepared a dataset specifically for training
these models to identify and locate biases in texts. Our evaluations across
various datasets demonstrate CBDT \textcolor{green} effectiveness in
distinguishing biased narratives from neutral ones and identifying specific
biased terms. This work paves the way for applying the CBDT \textcolor{green}
model in various linguistic and cultural contexts, enhancing its utility in
bias detection efforts. We also make the annotated dataset available for
research purposes.
