This repository provides a fine-tuned BERT model for Chinese Named Entity Recognition (NER). It is intended for researchers and developers working with Chinese NLP tasks who need a robust NER solution. The project offers a practical implementation for leveraging pre-trained BERT models for NER, achieving high accuracy.

How It Works

The project fine-tunes a pre-trained BERT model using the BIO data annotation scheme. It takes a pre-trained BERT model and a dataset formatted for NER tasks, then trains the model to identify and classify named entities within Chinese text. The approach leverages BERT's powerful contextual embeddings to achieve state-of-the-art performance.

Quick Start & Requirements

• Install/Run: python BERT_NER.py --data_dir=data/ --bert_config_file=checkpoint/bert_config.json --init_checkpoint=checkpoint/bert_model.ckpt --vocab_file=vocab.txt --output_dir=./output/result_dir/
• Prerequisites: BERT-TF source code, BERT-Base Chinese model checkpoint, data in BIO format.
• Setup: Requires downloading BERT source and model checkpoints.

Highlighted Details

• Achieved an evaluation F1 score of 0.966.
• Uses the classic People's Daily dataset for training.
• Supports fine-tuning BERT for Chinese NER.

Maintenance & Community

The project appears to be a personal exploration, with a note suggesting migration to an ALBERT fine-tune NER model. No community links or active maintenance signals are present.

Licensing & Compatibility

The license is not explicitly stated in the README. Compatibility for commercial use or closed-source linking is undetermined.

Limitations & Caveats

The project is presented as an experimental attempt and may not be actively maintained, with a recommendation to use an ALBERT-based model instead. The README does not specify the exact BERT version or framework used (e.g., TensorFlow, PyTorch).