BERT, or Bidirectional Encoder Representations from Transformers, is a revolutionary natural language processing (NLP) model that has transformed the field of NLP. This tutorial will provide an overview of BERT, its architecture, and how to use it for various NLP tasks.

Overview

BERT is designed to pre-train deep bidirectional representations from unlabeled text. By pre-training on a large corpus of text, BERT can then be fine-tuned on specific tasks to achieve state-of-the-art performance.

Key Features

  • Bidirectional Training: BERT is trained to understand the context of a word by considering its surrounding words, unlike traditional NLP models that are typically unidirectional.
  • Transformer Architecture: BERT uses the Transformer architecture, which has been proven to be effective for NLP tasks.
  • Pre-training and Fine-tuning: BERT is pre-trained on a large corpus of text and can then be fine-tuned on specific tasks.

Architecture

The architecture of BERT consists of two main components: the Transformer encoder and the pre-training tasks.

Transformer Encoder

The Transformer encoder is a stack of self-attention layers and feed-forward neural networks. Each layer in the encoder processes the input sequence and generates a contextual representation for each word.

Pre-training Tasks

BERT is pre-trained on two tasks: Masked Language Model (MLM) and Next Sentence Prediction (NSP).

  • Masked Language Model (MLM): In this task, some words in the input sequence are randomly masked, and the model is trained to predict the masked words.
  • Next Sentence Prediction (NSP): In this task, the model is trained to predict whether two sentences are related or not.

Usage

BERT can be used for various NLP tasks, such as text classification, sentiment analysis, named entity recognition, and more.

Text Classification

To use BERT for text classification, you can fine-tune the pre-trained model on your specific dataset. Here's an example:

from transformers import BertTokenizer, BertForSequenceClassification
import torch

# Load pre-trained model and tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')

# Tokenize input text
inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")

# Forward pass
outputs = model(**inputs)

# Get predictions
predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)

Further Reading

For more information on BERT, you can refer to the following resources:

BERT Architecture