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This article is divided into two parts; they are: • Architecture and Training of BERT • Variations of BERT BERT is an encoder-only model.

This article is divided into four parts; they are: • Preparing Documents • Creating Sentence Pairs from Document • Masking Tokens • Saving the Training Data for Reuse Unlike decoder-only models, BERT’s pretraining is more complex.

Machine learning models often behave differently across environments.

Clustering models in machine learning must be assessed by how well they separate data into meaningful groups with distinctive characteristics.

This article is divided into three parts; they are: • Creating a BERT Model the Easy Way • Creating a BERT Model from Scratch with PyTorch • Pre-training the BERT Model If your goal is to create a BERT model so that you can train it on your own data, using the Hugging Face `transformers`

Large language models (LLMs) are based on the transformer architecture, a complex deep neural network whose input is a sequence of token embeddings.

This article is divided into two parts; they are: • Fine-tuning a BERT Model for GLUE Tasks • Fine-tuning a BERT Model for SQuAD Tasks GLUE is a benchmark for evaluating natural language understanding (NLU) tasks.

This article is divided into four parts; they are: • Optimizers for Training Language Models • Learning Rate Schedulers • Sequence Length Scheduling • Other Techniques to Help Training Deep Learning Models Adam has been the most popular optimizer for training deep learning models.

Large language models (LLMs) are mainly trained to generate text responses to user queries or prompts, with complex reasoning under the hood that not only involves language generation by predicting each next token in the output sequence, but also entails a deep understanding of the linguistic patterns surrounding the user input text.