classical chinese punctuation guwen biaodian

Introduction

The Classical Chinese Punctuation model is designed to punctuate Classical (ancient) Chinese texts. This task is significant as historical Chinese texts often lack punctuation, presenting challenges in comprehension and analysis. This model leverages natural language processing (NLP) techniques typically used in Named Entity Recognition (NER) tasks to address this issue, utilizing abundant labeled data derived from existing punctuated texts.

Architecture

The model is built using the Transformers library and PyTorch, focusing on token classification for punctuation insertion. It is compatible with BERT architectures and supports Chinese language processing, particularly for ancient texts such as 文言文 (Wenyanwen).

Training

The training process involves utilizing both unpunctuated ancient Chinese texts and their punctuated counterparts. Through regex operations and existing literature, the model is trained to recognize patterns and insert appropriate punctuation marks, supporting over twenty different punctuation symbols.

Guide: Running Locally

1. Install Dependencies: Ensure Python is installed along with necessary libraries such as Transformers and PyTorch.
2. Download Model: Access the model from Hugging Face and download it locally.
3. Prepare Data: Input your unpunctuated Classical Chinese text.
4. Run Inference: Use the model to process the text and add punctuation.

For optimal performance, especially with large datasets, consider using cloud GPUs from providers like AWS or Google Cloud.

License

The project and its resources are available on GitHub, where users can contribute and suggest improvements. The repository encourages collaboration and is open for community engagement.