# Pre-trained Language Models

NEZHA is a neural contextualized representation model for Chinese understanding, based on the Transformer architecture, developed by Huawei Noah's Ark Lab.

BROS is a pre-trained language model focused on text and layout understanding, designed to efficiently extract key information from documents.

This model is trained using the Longformer architecture, specifically designed to detect machine-paraphrased plagiarized text, with significant application value in maintaining academic integrity.

A biomedical entity recognition model based on BioBERT developed by Dmis-lab at Korea University, specializing in feature extraction tasks for the NCBI disease dataset

CAMeLBERT is a collection of BERT models optimized for Arabic language variants, with the CA version specifically pre-trained on Classical Arabic texts