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:

  1. 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.
  2. 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.
  3. 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.
  4. 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.