Open LLM List
question answering chinese
yechenQuestion Answering Chinese Model
Introduction
This repository hosts a Chinese question-answering model based on BERT architecture. It is designed to perform question-answering tasks specifically for the Chinese language. The model leverages popular machine learning frameworks such as PyTorch and TensorFlow and is compatible with JAX for diverse deployment options.
Architecture
The model utilizes the BERT architecture, which is well-suited for natural language processing tasks, including question answering. BERT's bidirectional training approach allows it to understand the context of a word based on surrounding words, making it effective for language-specific tasks like Chinese question answering.
Training
Training details are not explicitly mentioned in the README. However, typical training of BERT-based models involves fine-tuning on a question-answering dataset, which would include Chinese language datasets in this case. This process fine-tunes the model to understand and accurately predict answers to questions based on provided context.
Guide: Running Locally
- Clone the Repository: Download the model and its associated files by cloning the repository.
git clone https://github.com/yechen/question-answering-chinese.git - Set Up Environment: Install necessary dependencies. It's recommended to use a virtual environment.
cd question-answering-chinese pip install -r requirements.txt - Download Pre-trained Model: Ensure you have the pre-trained weights, which might be available in the repository or through Hugging Face's model hub.
- Inference: Run the model using the provided scripts or your custom script to perform inference on your data.
For faster processing, especially for large datasets or complex queries, consider using cloud GPU services such as AWS, Google Cloud, or Azure, which offer scalable resources suited for such tasks.
License
Details about the license are not provided in the README. It is recommended to check the repository for any licensing files or notes to understand the terms under which this model can be used, modified, or shared.
