BERT, or Bidirectional Encoder Representations from Transformers, is a deep learning technique for natural language processing. It has become the standard for many NLP tasks, thanks to its ability to understand context in a sentence.
Key Concepts
- Bidirectional: BERT processes text from both left and right contexts, which helps in understanding the context better.
- Transformer: BERT uses the Transformer architecture, which is a deep neural network based on self-attention mechanisms.
How BERT Works
BERT works by taking a sentence and generating a representation of the sentence that captures the context of each word. This representation is then used to predict labels or answer questions.
For example, consider the sentence: "I love to eat pizza."
BERT would understand that "love" is a sentiment word and "pizza" is a noun. It would also understand that "love" and "pizza" are related because they are part of the same sentence.
Applications
BERT is used in various applications such as:
- Text classification
- Named entity recognition
- Sentiment analysis
- Question answering
Resources
For more information on BERT, you can visit the official BERT GitHub repository.