Bidirectional Encoder Representations from Transformers

Bidirectional Encoder Representations from Transformers (BERT) used on some significant ideas of models developed in the past.

Overview of Bidirectional Encoder Representations from Transformers

  • The concept of transfer learning
  • It is only for the models which are without RNNs.
  • Give attention to a very small ngram (or subwords) vocab also gives it an advantage considering the perspective of the memory. It is built on current technologies like Transfer learning, Generative Pre-training, semi-supervised learning, ELMo, and ULMFit.

Comparison with other Bidirectional Unsupervised Models

It is the first of its kind, a deeply bidirectional unsupervised model that is trained using any simple corpus. It is built on PyTorch.

Bidirectional Encoder Representations from Transformers Uses

It can be used for all text analytical tasks such as sentiment detection, classification, machine translation, named entity recognition, summarization, and question answering.