what is bert

 BERT (Bidirectional Encoder Representations from Transformers) is a deep learning model developed by Google in 2018 that revolutionized natural language processing (NLP). It is based on the Transformer architecture and has significantly improved the state of the art in various NLP tasks, such as sentiment analysis, question answering, and named entity recognition.

Key Concepts of BERT:

1. Transformer Architecture:

 BERT is built on the Transformer architecture, introduced by Vaswani et al. in 2017. The Transformer model relies on self-attention mechanisms to process input data in a parallelized manner, which is more efficient than traditional RNNs and LSTMs.

2. Bidirectional Training:

Unlike previous models like GPT (which is unidirectional and processes text from left to right), BERT is bidirectional. This means BERT looks at both the left and right context simultaneously when processing words. This bidirectional approach allows BERT to capture richer contextual information.

3. Pre-training and Fine-tuning:

Pre-training: BERT is pre-trained on a large corpus of text in an unsupervised manner. It is trained on two main tasks:

  • Masked Language Modeling (MLM): Randomly masks some tokens in the input and trains the model to predict those masked tokens based on the surrounding context.
  • Next Sentence Prediction (NSP): Trains the model to understand the relationship between two sentences by predicting whether a given sentence pair is consecutive or not.
Fine-tuning: After pre-training, BERT can be fine-tuned on specific tasks using a much smaller labeled dataset. During fine-tuning, BERT’s parameters are adjusted to optimize performance on the target task.

4. WordPiece Tokenization:

BERT uses a technique called WordPiece tokenization, which breaks words into smaller subword units (tokens). This helps BERT handle rare and unseen words effectively.



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