chinese pert large mrc

Introduction

The Chinese PERT-Large-MRC is a machine reading comprehension model designed for the Chinese language. It is built on the PERT-large architecture and fine-tuned using a combination of Chinese MRC datasets. The model specializes in understanding and answering questions based on given texts.

Architecture

The model utilizes the PERT architecture, which is a pre-trained model based on a permuted language model (PerLM). This approach allows the model to learn semantic information in a self-supervised manner without using mask tokens [MASK]. PERT effectively handles tasks like reading comprehension and sequence labeling by permuting input sentences to understand contextual semantics.

Training

The Chinese PERT-Large-MRC model has been fine-tuned on a variety of Chinese MRC datasets. Performance metrics, such as Exact Match (EM) and F1 scores, are reported for datasets like CMRC 2018, DRCD, and SQuAD-Zen. For instance, on the CMRC 2018 development set, the model achieved an EM/F1 score of 73.5/90.8.

Guide: Running Locally

To run the Chinese PERT-Large-MRC model locally, follow these steps:

1. Install Dependencies: Ensure that you have Python and PyTorch installed. You may also need the transformers library from Hugging Face.
2. Load the Model: Use BertForQuestionAnswering from the transformers library to load the model.
3. Prepare Data: Format your input data according to the requirements of a question-answering task.
4. Inference: Run inference using the model to get answers based on your input data.

For optimal performance, using a cloud GPU service such as Google Cloud, AWS, or Azure is recommended, especially when working with large models and datasets.

License

The Chinese PERT-Large-MRC model is licensed under the Apache 2.0 License, allowing for free use, modification, and distribution under the terms of the license.