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DistilBERT is a light version of BERT [178] proposed to mitigate BERT limitations, such as computational complexity, fixed input length size and word piece embedding problem. DistilBERT has the same architecture as BERT, but with additional steps as the number of layers is reduced, token type embedding, and the pooler are removed. In our work, we used the DistillBERT model consisted of 6 layered transformer blocks, where each block contained 12 self-attention layers and 768 hidden layers. We tokenized the input texts and convert the tokens into input IDS. Then, we padded and fed the input IDs into the DistilBERT model for a binary classification task. In this section, we specifically discuss the performance analyses of deep learning models and transformers architectures. To do these experiments, we used the same parameters according to the original proposed architecture. We divided each dataset into training, validation and testing to perform the experiments. Based on these predefined parameters, evaluate these algorithms performance in fake review detection in terms of performance accuracy, precision, recall, and F1-score as described in Table 13.
