  This paper focuses on detecting propagandistic spans and persuasion
techniques in Arabic text from tweets and news paragraphs. Each entry in the
dataset contains a text sample and corresponding labels that indicate the start
and end positions of propaganda techniques within the text. Tokens falling
within a labeled span were assigned "B" (Begin) or "I" (Inside), "O",
corresponding to the specific propaganda technique. Using attention masks, we
created uniform lengths for each span and assigned BIO tags to each token based
on the provided labels. Then, we used AraBERT-base pre-trained model for Arabic
text tokenization and embeddings with a token classification layer to identify
propaganda techniques. Our training process involves a two-phase fine-tuning
approach. First, we train only the classification layer for a few epochs,
followed by full model fine-tuning, updating all parameters. This methodology
allows the model to adapt to the specific characteristics of the propaganda
detection task while leveraging the knowledge captured by the pre-trained
AraBERT model. Our approach achieved an F1 score of 0.2774, securing the 3rd
position in the leaderboard of Task 1.
