Pre-train a costumize BERT with Masked Language Modeling objective on Persian text data using Hugging Face library. For training on unlabeld text, one great source is https://huggingface.co/datasets/SLPL/naab dataset, contating almost 130 GB of persian unlabeld pre-processed text. The process of pre-training a language model from scratch involves these main steps:
1- Train a Custom Tokenizer on the Text Data: Utilize a Word Piece tokenizer specifically designed for BERT models to train on the provided text data.
2- Tokenize the Dataset using the Trained Custom Tokenizer: Apply the trained custom tokenizer to tokenize the dataset.
3- Configure and Initialize a Custom BERT Model with Masked Language Modeling Objective: Configure and initialize a custom BERT model, focusing on the Masked Language Modeling objective. Alternative objectives may be considered, but it is crucial to ensure compatibility with the appropriate dataset.
4- Define Training Arguments and Commence Training: Establish the necessary training arguments and initiate the training process.