albert tiny chinese pos

Introduction

The CKIP ALBERT TINY CHINESE project provides traditional Chinese transformer models, including ALBERT, BERT, and GPT2, along with natural language processing tools such as word segmentation, part-of-speech tagging, and named entity recognition.

Architecture

This project utilizes the ALBERT architecture, a lightweight variant of BERT designed for efficiency, particularly in token classification tasks. It is built using the PyTorch library and supports the Chinese language.

Training

Training details are not explicitly provided in the documentation. However, the models are pre-trained and available for use through the Hugging Face Model Hub. Users are encouraged to use BertTokenizerFast for tokenization tasks.

Guide: Running Locally

1. Setup Environment:
   • Install the transformers library from Hugging Face:

     pip install transformers
     

2. Load Model and Tokenizer:
   • Use BertTokenizerFast and the model from Hugging Face:

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

3. Inference:
   • Tokenize input text and perform inference using the loaded model.

For better performance, consider using cloud GPU services like AWS, Google Cloud, or Azure to run the model.

License

This project is licensed under the GPL-3.0 license, which allows for redistribution and modification under the same license terms.