bert-bilstm-crf

BERT-BiLSTM-CRF is a natural language processing (NLP) model that combines three different neural network architectures to perform named entity recognition (NER) tasks.

BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained language model that uses a transformer-based architecture to learn contextual representations of words. It is bidirectional, meaning that it considers both the left and right context of each word when generating its embeddings.

BiLSTM (Bidirectional Long Short-Term Memory) is a type of recurrent neural network that processes sequences in both forward and backward directions. This allows it to capture long-term dependencies and contextual information in the input.

CRF (Conditional Random Field) is a probabilistic model that is often used in sequence labeling tasks, such as NER. It calculates the most likely sequence of labels given the input sequence, taking into account the dependencies between neighboring labels.

In the BERT-BiLSTM-CRF model, the BERT embeddings are fed into a BiLSTM layer, which produces contextual embeddings. These embeddings are then passed through a CRF layer to predict the most likely sequence of named entities in the input text. This model has shown to achieve state-of-the-art results in NER tasks for various languages.