BERT
Bidirectional Encoder Representations from Transformers. Google's language model that reads text bidirectionally for better context understanding.
Key Concepts
Bidirectional
BERT reads text in both directions, which allows it to learn the context of words better than previous models.
Transformer
BERT is based on the Transformer architecture, which is a type of neural network that is well-suited for natural language processing tasks.
Pre-training
BERT is pre-trained on a massive amount of text data, which allows it to learn a general representation of language that can be fine-tuned for specific tasks.
Detailed Explanation
BERT (Bidirectional Encoder Representations from Transformers) is a large language model that was developed by Google in 2018. It is a major breakthrough in natural language processing, and it has achieved state-of-the-art results on a wide variety of tasks, including question answering, sentiment analysis, and named entity recognition.
BERT is a bidirectional model, which means that it reads text in both directions. This allows it to learn the context of words better than previous models, which only read text in one direction. BERT is also based on the Transformer architecture, which is a type of neural network that is well-suited for natural language processing tasks.
BERT is pre-trained on a massive amount of text data, which allows it to learn a general representation of language that can be fine-tuned for specific tasks. This process of pre-training and fine-tuning is what makes BERT so powerful.
Real-World Examples & Use Cases
Google Search
BERT is used in Google Search to better understand the meaning of queries.
Google Translate
BERT is used in Google Translate to improve the quality of translations.
Google Assistant
BERT is used in the Google Assistant to better understand and respond to user requests.