chinese bert wwm ext

Introduction

Chinese BERT with Whole Word Masking (WWM) is a pre-trained language model designed to enhance Chinese natural language processing tasks. Developed by the Joint Laboratory of Harbin Institute of Technology (HIT) and iFLYTEK Research (HFL), this model incorporates Whole Word Masking during pre-training to improve the understanding of Chinese text.

Architecture

The model builds on the original BERT architecture from Google, incorporating Whole Word Masking to better handle the intricacies of the Chinese language. This technique masks entire words instead of individual characters, leading to more context-aware representations.

Training

The training process utilizes Whole Word Masking, a method that masks entire words rather than individual Chinese characters, thus providing richer contextual information during the pre-training phase. The model is based on the BERT framework and can be found in the repository: Google BERT.

Guide: Running Locally

To run Chinese BERT with WWM locally, follow these steps:

1. Prerequisites: Ensure Python and PyTorch are installed on your machine.
2. Clone the Repository: Download the model repository from Chinese BERT WWM.
3. Install Dependencies: Use pip to install the required packages.
4. Download the Model: Use the provided scripts in the repository to download the pre-trained model weights.
5. Inference: Use the scripts to perform inference on your Chinese text data.

For heavy computations, consider using cloud GPUs such as those available from Google Cloud, AWS, or Azure to accelerate the process.

License

The Chinese BERT with Whole Word Masking is licensed under the Apache 2.0 License, allowing for broad use and modification of the software while requiring attribution and preserving the license.