coder_eng_pp

Introduction

CODER++ is a model designed for automatic biomedical term clustering by learning fine-grained term representations. It leverages BERT architecture and is implemented in PyTorch, focusing on feature extraction in the biomedical domain. The model is compatible with Inference Endpoints, making it versatile for various deployment scenarios.

Architecture

CODER++ is based on the BERT architecture, which is known for its transformer-based design. This model is specifically tuned for biomedical applications, allowing it to effectively handle the complexities of biomedical terminology.

Training

While specific training details are not provided in the summary, CODER++ is designed to learn fine-grained representations of biomedical terms, which suggests a specialized training regime on a biomedical corpus. The model is likely fine-tuned to enhance the clustering of similar terms.

Guide: Running Locally

1. Clone the Repository:

   git clone https://github.com/GanjinZero/CODER.git
   

2. Install Dependencies:
   Ensure you have Python and PyTorch installed. You may need additional packages specified in the repository.

3. Download the Model:
   Follow instructions in the repository to download the CODER++ model files.

4. Run Inference:
   Use PyTorch to load the model and perform inference on your data.

5. Cloud GPUs:
   For efficient processing, consider using cloud GPU services like AWS EC2, Google Cloud, or Azure.

License

CODER++ is licensed under the Apache-2.0 License, allowing for wide usage and modification within the bounds of this open-source license.