DistilKoBERT

Introduction

DistilKoBERT is a distilled version of the Korean BERT (KoBERT) model, designed to provide a more lightweight model with faster inference times while maintaining a high level of performance for Korean language processing tasks.

Architecture

DistilKoBERT leverages a similar architecture to the original BERT model but is streamlined to be more efficient. This is achieved through model distillation, a process that reduces the model size while retaining its capabilities. It is designed to work with the PyTorch library and supports the use of the safetensors format for secure handling of model weights.

Training

DistilKoBERT was trained using a smaller dataset and fewer parameters compared to the original KoBERT, through a process aimed at compressing the model while retaining its performance. This makes it suitable for applications needing quick and efficient language processing in Korean.

Guide: Running Locally

To run DistilKoBERT locally, follow these steps:

1. Install the Transformers library:

   pip install transformers
   

2. Import the Model and Tokenizer:

   from transformers import AutoModel, AutoTokenizer
   
   model = AutoModel.from_pretrained("monologg/distilkobert")
   tokenizer = AutoTokenizer.from_pretrained("monologg/distilkobert", trust_remote_code=True)
   

3. Perform Inference: Utilize the model for text processing tasks in Korean.

For enhanced performance, consider using a cloud GPU service such as AWS EC2, Google Cloud Platform, or Azure.

License

DistilKoBERT is released under the Apache 2.0 License, which allows for wide usage and modification with proper attribution.