Pretraining and Setting Up a BERT Model
Learn about the steps involved in pretraining, as well as the initial steps needed to fine-tune a BERT model.
We'll cover the following
- Initial steps for fine-tuning BERT
- Hardware constraints
- Installing the Hugging Face PyTorch interface for BERT
- Importing the modules
- Specifying CUDA as the device for torch
- Loading the dataset
- Creating sentences, label lists, and adding BERT tokens
- Activating the BERT tokenizer
- Processing the data
- Creating attention masks
- Splitting the data into training and validation sets
- Converting all the data into torch tensors
- Selecting a batch size and creating an iterator
BERT is a two-step framework. The first step is pretraining, and the second is fine-tuning, as shown in the diagram below:
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