The key components of BERT (Bidirectional Encoder Representations from Transformers) are as follows:

  1. Transformer Encoder: BERT is built upon a transformer-based architecture consisting of multiple transformer encoder layers. These layers encode the input sequences by attending to the contextual information in both the left and right directions.

  2. Tokenization: BERT tokenizes input text into subword tokens using the WordPiece algorithm. This allows BERT to handle out-of-vocabulary words by breaking them down into smaller, known subword units.

  3. Word Embeddings: BERT utilizes word embeddings to represent individual tokens in the input. These embeddings capture semantic and syntactic information of words, which forms the foundation for contextualized representations.

  4. Segment Embeddings: BERT introduces segment embeddings to differentiate between different sentences or segments within a single input. It helps BERT understand the relationships between sentences in tasks like sentence classification or question answering.

  5. Positional Embeddings: Since BERT does not rely on recurrent or convolutional structures, it incorporates positional embeddings to encode the position of each token in the input sequence. This retains positional information for the transformer layers to consider during training and inference.

  6. Multi-Head Attention: BERT employs multi-head attention mechanism in the transformer layers. It allows the model to attend to different parts of the input sequence simultaneously and capture different types of dependencies between words.

  7. Feed-Forward Neural Network: BERT incorporates feed-forward neural networks (FFNNs) within the transformer layers to process the attended representations obtained from the multi-head attention. The FFNN helps in capturing complex interactions and extracting higher-level features.

  8. Pretraining and Fine-tuning: BERT is pretrained on a large corpus of unlabeled data using two objectives: masked language model (MLM) and next sentence prediction (NSP). After pretraining, the model can be fine-tuned on various downstream tasks by adding task-specific layers and training the entire model with labeled data.

  These key components of BERT work together to enable the model to learn contextualized representations and capture the relationships between words in a given input sequence.