albert base chinese

Introduction

The CKIP ALBERT Base Chinese project offers traditional Chinese transformer models, including ALBERT, BERT, and GPT2, along with NLP tools for word segmentation, part-of-speech tagging, and named entity recognition.

Architecture

The model is based on the ALBERT (A Lite BERT) architecture, optimized for efficiency and performance. It leverages the PyTorch framework and is specifically tailored for the Chinese language.

Training

The model is pre-trained on a large corpus of Chinese text, allowing it to perform various natural language processing tasks effectively. The training setup involves using the BertTokenizerFast for tokenization.

Guide: Running Locally

1. Setup Environment: Ensure you have Python and PyTorch installed.
2. Install Transformers: Install the transformers library from Hugging Face.

   pip install transformers
   

3. Load Model and Tokenizer: Use BertTokenizerFast and AutoModel to load the model.

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

4. Cloud GPUs: For enhanced performance, consider using cloud-based GPUs such as those provided by AWS, Google Cloud, or Azure.

License

The project is licensed under the GPL-3.0 license, ensuring open access and the ability to modify the software according to the license terms.