macanv/BERT-BiLSMT-CRF-NER
BERT BiLSMT CRF NER
该项目是一个专为命名实体识别（NER）和文本分类设计的自然语言处理系统。它使用机器学习方法从原始文本中识别特定名称和关键信息，从而将非结构化内容组织为结构化格式。
该系统实现了多层架构，结合了用于嵌入的预训练 Transformer、用于序列建模的双向长短期记忆网络（Bi-LSTM）以及用于标签转换的条件随机场（CRF）。它通过在特定任务数据集上微调这些模型来支持迁移学习。
该项目包含使用指定配置和词汇表文件在自定义数据集上训练模型的能力。它还提供了一种将训练好的模型部署为网络服务的机制，允许通过 HTTP 服务器进行文本分类和实体识别。
Features
- Named Entity Recognition - Identifies and classifies specific names and key information from unstructured text.
- Bidirectional LSTM Models - Uses bidirectional LSTM layers to capture long-range dependencies within sentences.
- Classification Fine-Tuning - Adapts pre-trained transformers for classification tasks through gradient-based fine-tuning on custom data.
- Model Training - Provides a pipeline to train named entity recognition models using custom datasets and vocabulary files.
- Natural Language Processing - Uses machine learning to analyze and categorize human language data into structured formats.
- Pre-training Transfer Learning - Supports transfer learning by fine-tuning a pre-trained BERT model on task-specific datasets.
- Sequence Labeling Architectures - Employs a conditional random field (CRF) for structured sequence labeling to predict entity transitions.
- Text Classifiers - Implements text classification to organize unstructured content into predefined categories.
- Token Embedding Layers - Implements a BERT-based layer to map tokens to dense vector representations for downstream sequence modeling.
- Model-as-a-Service Platforms - Offers the capability to package the trained classifier as a scalable inference service.
- Inference Servers - Provides an HTTP server to deploy the trained model as a network service for external requests.
- Classification Service Deployers - Ships a classification service deployer that serves predictions over HTTP.
- NLP Tasks - NER implementation combining BERT with Bi-LSTM-CRF architectures.
