bert base chinese ner

Introduction

The CKIP BERT BASE CHINESE project offers traditional Chinese transformers models, including ALBERT, BERT, and GPT2, as well as NLP tools like word segmentation, part-of-speech tagging, and named entity recognition.

Architecture

The models are built using the BERT architecture for token classification tasks in the Chinese language. They are optimized for PyTorch and JAX libraries, making them suitable for integration into various machine learning workflows.

Training

The training specifics for these models leverage the BERT architecture to handle Chinese text, focusing on tasks like token classification. The training process involves using large datasets to fine-tune the model for accurate named entity recognition in the Chinese language.

Guide: Running Locally

1. Install dependencies: Ensure that you have Python and the Transformers library installed.
2. Load the tokenizer and model:

   from transformers import BertTokenizerFast, AutoModel
   
   tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese')
   model = AutoModel.from_pretrained('ckiplab/bert-base-chinese-ner')
   

3. Inference: Use the tokenizer and model to process and predict on your input text.
4. Cloud GPUs: For enhanced performance, consider using cloud GPU services such as AWS, Google Cloud, or Azure to handle large-scale data.

License

This project is licensed under the GPL-3.0 license, allowing for the redistribution and modification of the software under the same terms.