bert4ner-base-chinese
| Property | Value |
|---|---|
| Author | shibing624 |
| Task | Named Entity Recognition |
| Language | Chinese |
| Model Base | BERT |
| Performance | 95.25% F1 Score |
What is bert4ner-base-chinese?
bert4ner-base-chinese is a specialized BERT-based model designed for Chinese Named Entity Recognition (NER). Built on the foundation of BERT architecture, this model can effectively identify and classify named entities in Chinese text, including person names (PER), locations (LOC), organizations (ORG), and time expressions (TIME). The model achieves state-of-the-art performance with an impressive F1 score of 95.25% on the People's Daily (PEOPLE) test dataset.
Implementation Details
The model implements a BertSoftmax architecture, utilizing the original BERT structure for token classification. It processes Chinese text at the character level and applies BIO (Beginning, Inside, Outside) tagging scheme to identify entity boundaries. The model can be easily integrated using either the nerpy library or directly through HuggingFace Transformers.
- Trained on high-quality datasets including CNER (120,000 characters) and PEOPLE (2 million characters)
- Supports multiple entity types: PER, LOC, ORG, TIME
- Includes complete model files: config.json, model_args.json, pytorch_model.bin, and necessary tokenizer files
Core Capabilities
- Accurate entity recognition with 94.25% accuracy and 96.27% recall
- Seamless integration with both nerpy and HuggingFace frameworks
- Character-level Chinese text processing
- Support for batch processing and efficient inference
Frequently Asked Questions
Q: What makes this model unique?
The model's uniqueness lies in its optimization for Chinese NER tasks, achieving near SOTA performance on the PEOPLE dataset while maintaining easy deployment options through multiple frameworks. Its BertSoftmax architecture ensures reliable entity recognition across various Chinese text formats.
Q: What are the recommended use cases?
The model is ideal for applications requiring Chinese named entity extraction, such as information extraction systems, content analysis, document processing, and automated text understanding. It's particularly effective for identifying person names, locations, organizations, and time expressions in Chinese text.
