A bi-directional LSTM (Long Short-Term Memory) is a type of recurrent neural network (RNN) architecture that processes data in both forward and backward directions. This dual processing allows the model to capture context from past and future states, making it particularly effective for tasks that require an understanding of the entire input sequence, such as named entity recognition. By leveraging information from both directions, bi-directional LSTMs enhance the model's ability to identify and classify entities more accurately in text data.
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