Update README.md

README.md

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 license: apache-2.0
 ---
 
-training named entity recognizer example: https://colab.research.google.com/drive/1WirfVhJIbKH70stuLRPhiPr2CexZiGuP?usp=sharing
+# Korean Character BERT Model
+
+Welcome to the repository of the Korean Character (syllable-level) BERT Model, a compact and efficient transformer-based model designed specifically for Korean language processing tasks. This model takes a unique approach by tokenizing text at the syllable level, catering to the linguistic characteristics of the Korean language.
+
+## Features
+
+- Vocabulary Size: The model utilizes a vocabulary of 7,477 tokens, focusing on Korean syllables. This streamlined vocabulary size allows for efficient processing while maintaining the ability to capture the nuances of the Korean language.
+- Transformer Encoder Layers: It employs a simplified architecture with only 3 transformer encoder layers. This design choice strikes a balance between model complexity and computational efficiency, making it suitable for a wide range of applications, from mobile devices to server environments.
+- License: This model is open-sourced under the Apache License 2.0, allowing for both academic and commercial use while ensuring that contributions and improvements are shared within the community.
+
+## Getting Started
+
+```
+# Load model directly
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+
+tokenizer = AutoTokenizer.from_pretrained("MrBananaHuman/char_ko_bert_small")
+model = AutoModelForMaskedLM.from_pretrained("MrBananaHuman/char_ko_bert_small")
+```
+training named entity recognizer example: https://colab.research.google.com/drive/1WirfVhJIbKH70stuLRPhiPr2CexZiGuP?usp=sharing
+
+## Contact
+
+For any questions or inquiries, please reach out to me at mrbananahuman.kim@gmail.com. I'm always happy to discuss the model, potential collaborations, or any other inquiries related to this project.