How does BERT work?

BERT Architecture

BERT is based on the Transformer architecture, introduced by Vaswani et al. in 2017. The key innovation of Transformers is the self-attention mechanism, which allows the model to weigh the significance of different words in a sentence relative to each other. BERT utilizes the encoder part of the Transformer which is comprised of layers of self-attention and feed-forward neural networks.

Here is a simplified diagram of BERT's architecture:

graph TD;
    A[Input Tokens] --> B[Token Embeddings];
    B --> C[Position Embeddings];
    C --> D[Segment Embeddings];
    D --> E[Transformer Encoder];
    E --> F[Output Representations];

Pretraining Objectives

1. Masked Language Model (MLM): Unlike traditional left-to-right language models, BERT masks some of the input tokens at random and trains the model to predict them from the context provided by the unmasked tokens. This allows BERT to learn bidirectional representations.

   • For example, in the sentence "The cat sat on the [MASK]", BERT predicts "mat" using context from both directions.

2. Next Sentence Prediction (NSP): This task helps BERT understand the relationship between two sentences. During pretraining, BERT is given pairs of sentences and learns to predict if the second sentence is a natural continuation of the first.

Fine-tuning Process

Fine-tuning involves training the pre-trained BERT model on task-specific data. Because BERT is already well-versed in language understanding, this process is relatively quick and involves adding a small layer for the specific task.

• Practical Applications: BERT can be fine-tuned for a variety of NLP tasks like sentiment analysis, named entity recognition, and question answering. For example, for sentiment analysis, a classification layer is added on top of BERT to predict sentiment labels.

Practical Example

Here's a simplified example of how you might fine-tune BERT for sentiment analysis using Python's transformers library:

from transformers import BertTokenizer, BertForSequenceClassification
from transformers import Trainer, TrainingArguments

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')

train_args = TrainingArguments(output_dir='./results', num_train_epochs=3, per_device_train_batch_size=16)
trainer = Trainer(model=model, args=train_args, train_dataset=train_dataset)

trainer.train()

References

• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
• The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning)

BERT has significantly advanced the state-of-the-art for a wide variety of NLP tasks and showcases the powerful capabilities of transformer models in understanding human language.