macanv/BERT-BiLSTM-CRF-NER
BERT BiLSTM CRF NER
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 architecture that exposes trained models via an HTTP API. This allows for remote inference, named entity prediction, and text document classification through a network interface.
Features
- Named Entity Recognition - Identifies and categorizes specific entities within unstructured text using BERT and BiLSTM-CRF models.
- BERT NER Frameworks - Implements a complete NER system using BERT, BiLSTM, and CRF layers for sequence labeling.
- Language Model Fine-Tuning - Adjusts pre-trained language model parameters using task-specific data to improve entity recognition.
- Text Document Classification - Assigns predefined labels to documents and text segments using a trained neural network.
- Task-Specific Fine-Tuning Pipelines - Provides a workflow for adapting BERT models to specific entity recognition tasks.
- NER Training Tools - Provides tools to fine-tune language models and sequence labeling networks for entity recognition.
- Conditional Random Fields - Applies a Conditional Random Field layer to predict the most likely sequence of entity labels.
- Bidirectional LSTMs - Implements bidirectional sequence modeling to capture text dependencies for entity recognition.
- TensorFlow Model Development - Built as a deep learning implementation for text processing and entity extraction using the TensorFlow ecosystem.
- Token Embedding Layers - Utilizes a BERT-based embedding layer to convert raw text into dense contextual vector representations.
- BERT Fine-Tuning - Adapts pre-trained BERT models for specialized text classification and entity recognition tasks.
- Model-as-a-Service Platforms - Exposes trained text models via an HTTP API for remote prediction requests.
- Model Serving & Deployment - Serves trained named entity recognition models through an HTTP API for remote inference.
- Inference APIs - Provides a network interface via HTTP for remote text classification and model inference.
- Model Inference APIs - Exposes named entity recognition models via an HTTP-based client-server architecture for remote predictions.
- Information Extraction - TensorFlow implementation of BERT-based NER.
