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Neural network layers that process sequences in both directions to capture long-term dependencies.
Distinct from Sequence-to-Sequence Mappings: Specializes sequence-to-sequence mapping to bidirectional LSTM architectures for contextual understanding.
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This project is a named entity recognition framework and TensorFlow-based natural language processing model. It provides a pipeline for adapting pre-trained language models to specific entity recognition and text classification tasks. The system implements a sequence labeling architecture that combines transformer-based embeddings with bidirectional sequence modeling and conditional random field decoding. It includes tools for fine-tuning model weights and training the network to identify and categorize entities within unstructured text. The framework also includes a client-server architectu
Implements bidirectional sequence modeling to capture text dependencies for entity recognition.