KoGPT2-Base-V2

Introduction

KoGPT2-Base-V2 is a Korean language model based on the GPT-2 architecture. Developed by SK Telecom, it is designed for text generation tasks and is available on Hugging Face's model hub. The model supports multiple frameworks, including Transformers, PyTorch, and JAX.

Architecture

KoGPT2-Base-V2 utilizes the GPT-2 architecture, which is known for its capability in generating coherent and contextually relevant text. It is specifically tailored for the Korean language, leveraging the strengths of GPT-2 in text generation.

Training

The model has been trained on a substantial corpus of Korean text, enabling it to perform various text generation tasks effectively. While specific details about the training data and methodology are not provided here, such information can typically be found in the associated GitHub repository or model card documentation.

Guide: Running Locally

To run KoGPT2-Base-V2 locally, follow these steps:

1. Install Dependencies: Ensure you have Python and the necessary libraries installed. You can install the Hugging Face Transformers library using pip:

   pip install transformers
   

2. Load the Model: Use the Transformers library to load and use the model:

   from transformers import GPT2Tokenizer, GPT2LMHeadModel
   
   tokenizer = GPT2Tokenizer.from_pretrained('skt/kogpt2-base-v2')
   model = GPT2LMHeadModel.from_pretrained('skt/kogpt2-base-v2')
   

3. Generate Text: Tokenize your input text and generate predictions:

   input_ids = tokenizer.encode("Your input text here", return_tensors='pt')
   output = model.generate(input_ids)
   print(tokenizer.decode(output[0], skip_special_tokens=True))
   

4. Cloud GPUs: For resource-intensive tasks, consider using cloud GPU services such as AWS, GCP, or Azure to enhance performance and reduce computation time.

License

KoGPT2-Base-V2 is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). This license allows for sharing and adapting the model for non-commercial purposes, provided appropriate credit is given and adaptations are shared under the same terms.