BERT (Bidirectional Encoder Representations from Transformers)

BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained language representation model that captures context-dependent word embeddings by considering the entire context of a word in both directions. It utilizes the transformer architecture and is trained on massive amounts of data to learn contextualized representations of words. Here's a simplified manual example to help understand the basic concept of BERT:

Example Sentence:

"The quick brown fox jumped over the lazy dog."
Step 1: Tokenization:
Tokenize the sentence into individual words or subwords using WordPiece tokenization. This step may break words into subwords to handle out-of-vocabulary words efficiently.
["[CLS]", "The", "quick", "brown", "fox", "jumped", "over", "the", "lazy", "dog", ".", "[SEP]"]

Here, [CLS] and [SEP] are special tokens representing the start and end of the sentence, respectively.
Step 2: Embedding Layer:
Initialize embeddings for each token. In BERT, each token has three embeddings: positional embedding, token type embedding (segment embedding), and the WordPiece embedding.
Positional Embedding: Represent the position of each token in the sequence to capture its position-dependent meaning.
Token Type Embedding (Segment Embedding): If there are multiple segments in the input (e.g., in question answering, where the question and answer are separate segments), this embedding helps distinguish between them.
WordPiece Embedding: Represent the meaning of the token itself.
Example Embeddings for "quick":
Positional Embedding: [0.2, -0.1, 0.3, ...]
Token Type Embedding: [0.1, 0.5, -0.2, ...]
WordPiece Embedding: [0.4, -0.3, 0.6, ...]
Step 3: Transformer Layers:
Apply multiple transformer layers to capture contextual relationships between words. Each layer consists of self-attention mechanisms, allowing the model to consider all positions in the input sequence simultaneously.
Example Transformer Layer Output for "quick":
$\text{Attention output}=\text{Attention}(\text{Embeddings})$
Step 4: Pooling:
Aggregate information from all tokens using pooling techniques. For example, the [CLS] token's output may be used as a representation of the entire sentence.
Example Sentence Representation:
$\text{Sentence Representation}=\text{Pooling}(\text{[CLS] Token Output})$
Step 5: Task-Specific Layer:
Fine-tune the model for specific tasks by adding a task-specific layer. For example, in a text classification task, a softmax layer may be added for predicting class labels.
Example Classification Output:
$\text{Class Probabilities}=\text{Softmax}(\text{Task-Specific Layer Output})$
BERT's pre-training process involves predicting masked words within a sentence and determining the relationship between two sentences (Next Sentence Prediction). During fine-tuning, the model is adapted to specific downstream tasks.
This is a high-level overview, and the actual BERT model involves many intricacies and advanced techniques. BERT has achieved state-of-the-art performance in various natural language processing tasks by capturing rich contextual information.