chinese bert wwm

Introduction

Chinese BERT with Whole Word Masking is a pre-trained language model designed to enhance Chinese natural language processing tasks. Developed by the Joint Laboratory of HIT and iFLYTEK Research (HFL), this model leverages whole word masking strategies to improve the performance of BERT in understanding Chinese text.

Architecture

The model is an adaptation of Google Research's BERT, specifically tailored for Chinese language processing. It incorporates whole word masking, a technique where entire words, rather than individual characters, are masked during pre-training. This approach helps the model understand contextual relationships within Chinese text more effectively.

Training

The training process for Chinese BERT with Whole Word Masking involves comprehensive pre-training on large-scale Chinese corpora. The model is optimized to capture intricate nuances and linguistic patterns unique to Chinese, enhancing its ability to perform various natural language processing tasks such as text classification, named entity recognition, and machine translation.

Guide: Running Locally

1. Clone the Repository:

   git clone https://github.com/google-research/bert
   cd bert
   

2. Install Dependencies:
   Ensure you have Python and necessary libraries installed. Install dependencies via pip:

   pip install -r requirements.txt
   

3. Download Pre-trained Model:
   Access the pre-trained Chinese BERT model from Hugging Face or other hosting services.

4. Run Inference or Training:
   Use scripts provided within the repository to perform tasks like inference or further training.

5. Utilize Cloud GPUs:
   For intensive tasks, consider using cloud-based GPU services like AWS EC2, Google Cloud, or Azure to leverage powerful hardware.

License

The Chinese BERT with Whole Word Masking model is licensed under the Apache 2.0 License. This permits broad use, modification, and distribution, provided that the terms of the license are met.