BERT(트랜스포머의 양방향 인코더 표현)

Bert의 작동 방식

Unlike earlier models that processed text in a single direction (either left-to-right or right-to-left), BERT processes the entire sequence of words at once using its Transformer encoder and the self-attention mechanism. This bidirectional approach allows it to grasp the context of a word based on its surrounding words, both preceding and following it. For instance, BERT can differentiate the meaning of "bank" in "I need to go to the bank to withdraw cash" versus "The river bank was muddy" by considering the full sentence context.

BERT learns these complex language relationships during a pre-training phase on vast amounts of text data. This involves two main unsupervised tasks:

1. Masked Language Model (MLM): Some percentage of input tokens (words or sub-words) are randomly masked (hidden), and the model learns to predict these masked tokens based on their context.
2. Next Sentence Prediction (NSP): The model receives pairs of sentences and learns to predict if the second sentence is the actual next sentence that follows the first one in the original text, or just a random sentence.

The result of this pre-training is a model with rich language embeddings that capture syntax and semantics. This pre-trained BERT model can then be quickly adapted or 'fine-tuned' for various specific downstream NLP tasks using smaller, task-specific datasets. This process of leveraging pre-trained knowledge is a form of transfer learning.

주요 기능 및 이점

• Deep Bidirectional Context: BERT's primary innovation is its ability to understand the context of a word by looking at both the words that come before and after it simultaneously. This leads to a much richer and more accurate understanding of language nuances compared to unidirectional models like early versions of GPT.
• State-of-the-Art Performance: Upon its release, BERT achieved state-of-the-art results on a wide range of NLP benchmarks, including question answering (like the SQuAD dataset) and Natural Language Understanding (NLU) tasks.
• Transfer Learning Powerhouse: BERT's pre-trained models serve as a powerful foundation. By fine-tuning BERT on specific tasks like sentiment analysis or Named Entity Recognition (NER), developers can achieve high performance with significantly less task-specific data and training time compared to training a model from scratch.
• Wide Availability: Pre-trained BERT models are readily accessible through platforms like Hugging Face and can be used with popular Deep Learning (DL) frameworks such as PyTorch and TensorFlow.

실제 애플리케이션

BERT's ability to understand language nuances has led to significant improvements in various real-world Artificial Intelligence (AI) applications:

• Search Engines: Google Search famously incorporated BERT to better understand user queries, especially conversational or complex ones, leading to more relevant search results. As explained in a Google AI Blog post, BERT helps grasp the intent behind searches like "can you get medicine for someone pharmacy" by understanding the importance of prepositions like "for" and "to".
• Chatbots and Virtual Assistants: BERT enhances the ability of chatbots and virtual assistants to understand user requests more accurately, maintain context in conversations, and provide more helpful responses in customer service, booking systems, and information retrieval.
• Sentiment Analysis: Businesses use BERT-based models to analyze customer reviews, social media comments, and survey responses to gauge public opinion and product feedback with higher accuracy.
• Text Summarization and Question Answering: BERT can be fine-tuned to create systems that automatically summarize long documents (text summarization) or answer questions based on a given passage of text.

While BERT is primarily used in NLP, the Transformer architecture it popularized has also inspired advancements in Computer Vision (CV), such as Vision Transformers (ViT) used in models like RT-DETR. Platforms like Ultralytics HUB facilitate the training and deployment of various AI models, including those built on Transformer principles.