mengzi bert base

Introduction

Mengzi-BERT is a Chinese pre-trained language model designed to be lightweight yet powerful. It utilizes a 300G Chinese corpus for training tasks such as masked language modeling (MLM), part-of-speech (POS) tagging, and sentence order prediction (SOP).

Architecture

Mengzi-BERT is based on the BERT architecture, a transformer model widely used for various natural language processing tasks. It is optimized for Chinese language processing and supports fill-mask tasks, using the PyTorch library.

Training

The model was trained using a 300G corpus of Chinese text, employing techniques like MLM, POS tagging, and SOP. Mengzi-BERT has been evaluated on nine Chinese tasks, achieving competitive scores compared to other models like RoBERTa-wwm-ext.

Guide: Running Locally

1. Install the transformers library from Hugging Face using pip:

   pip install transformers
   

2. Use the following Python code to load the tokenizer and model:

   from transformers import BertTokenizer, BertModel
   
   tokenizer = BertTokenizer.from_pretrained("Langboat/mengzi-bert-base")
   model = BertModel.from_pretrained("Langboat/mengzi-bert-base")
   

3. For optimal performance, especially with larger datasets, consider using cloud GPU services like AWS, Google Cloud, or Azure.

License

Mengzi-BERT is distributed under the Apache-2.0 license, which allows for wide usage and modification with proper attribution.