nezha cn base

Introduction

NEZHA-CN-BASE is a model for understanding the Chinese language, based on the NEZHA architecture. It is optimized for tasks that require contextualized representations in Chinese, leveraging the PyTorch and Transformers libraries.

Architecture

NEZHA (Neural Contextualized Representation for Chinese Language Understanding) is developed to enhance language understanding capabilities for Chinese. It employs a sophisticated model architecture that builds upon BERT tokenizer classes and NEZHA model classes, providing a robust framework for processing Chinese text.

Training

The NEZHA model architecture was introduced in a research paper by Junqiu Wei et al., and further details can be found in the paper "NEZHA: Neural Contextualized Representation for Chinese Language Understanding" (arXiv:1909.00204). The original checkpoints for NEZHA are available on GitHub.

Guide: Running Locally

To run the NEZHA-CN-BASE model locally, follow these steps:

1. Install the transformers library:

   pip install transformers
   

2. Import the necessary classes and load the tokenizer and model:

   from transformers import BertTokenizer, NezhaModel
   tokenizer = BertTokenizer.from_pretrained('sijunhe/nezha-cn-base')
   model = NezhaModel.from_pretrained("sijunhe/nezha-cn-base")
   

3. Encode input text and generate model output:

   text = "我爱北京天安门"
   encoded_input = tokenizer(text, return_tensors='pt')
   output = model(**encoded_input)
   

For enhanced performance, consider using cloud GPU services from providers like AWS, Google Cloud, or Azure.

License

The NEZHA-CN-BASE model is distributed under the AFL-3.0 license.