About Google BERT
Google’s BERT (Bidirectional Encoder Representations from Transformers) is a state-of-the-art pre-training technique for natural language processing (NLP). It was open-sourced by Google in 2018 to help address the challenge of training data shortage in NLP.
Features of BERT:
- Deeply Bidirectional: Unlike previous models, BERT is deeply bidirectional, meaning it uses both the previous and next context of a word for representation. This allows BERT to understand the full context of a word based on all its surroundings.
- Pre-training on Large Text Corpus: BERT is pre-trained on a large plain text corpus (like Wikipedia). This pre-training helps in fine-tuning the model on specific NLP tasks like question answering and sentiment analysis, leading to substantial accuracy improvements.
- Masking Technique: BERT uses a technique of masking out some of the words in the input and then conditions each word bidirectionally to predict the masked words. This technique helps in training the model efficiently.
- Modeling Relationships Between Sentences: BERT also learns to model relationships between sentences by pre-training on a simple task that can be generated from any text corpus. This involves determining whether a given sentence B is the actual next sentence that comes after sentence A in the corpus.