bert kor base

Introduction

The BERT-KOR-BASE is a BERT-based language model specifically designed for the Korean language. It leverages a substantial Korean text dataset and a large vocabulary of lower-cased subwords to enhance its linguistic capabilities.

Architecture

The model is based on the BERT architecture, which is a transformer model known for its effectiveness in language representation tasks. It is pre-trained on a 70GB Korean text dataset and utilizes 42,000 lower-cased subwords to accommodate the unique characteristics of the Korean language.

Training

The training process involved the use of a massive Korean text corpus to ensure that the model captures the nuances of the language effectively. Detailed performance metrics and comparisons with other Korean language models can be found on the associated GitHub page.

Guide: Running Locally

To run the BERT-KOR-BASE model locally, follow these steps:

1. Install the Transformers library:

   pip install transformers
   

2. Load the tokenizer and model:

   from transformers import BertTokenizerFast, BertModel
   
   tokenizer_bert = BertTokenizerFast.from_pretrained("kykim/bert-kor-base")
   model_bert = BertModel.from_pretrained("kykim/bert-kor-base")
   

3. Inference: Use the tokenizer to process input text and pass it through the model for predictions.

For efficient training and inference, consider using cloud GPU services such as Google Cloud, AWS, or Azure for enhanced computational power.

License

The licensing terms for the BERT-KOR-BASE model have not been specified in the README. Please refer to the Hugging Face model page or associated GitHub repository for detailed licensing information.