kobart summarization

Introduction

The KOBART-Summarization model is designed for Korean text summarization. It leverages the BART architecture and is implemented using PyTorch. This model is suitable for text-to-text generation tasks and is compatible with various inference endpoints.

Architecture

KOBART-Summarization is based on the BART (Bidirectional and Auto-Regressive Transformers) architecture, which is effective for sequence-to-sequence text generation tasks. It uses the Transformers library and is implemented in PyTorch, supporting safe tensor operations.

Training

Details on the specific training process are not provided in the README. However, the model is designed to summarize Korean news articles efficiently, utilizing pre-trained weights tailored for the Korean language.

Guide: Running Locally

To run the KOBART-Summarization model locally, follow these steps:

1. Install Dependencies: Ensure you have PyTorch and the Transformers library installed.

   pip install torch transformers
   

2. Load the Model and Tokenizer:

   import torch
   from transformers import PreTrainedTokenizerFast, BartForConditionalGeneration
   
   tokenizer = PreTrainedTokenizerFast.from_pretrained('gogamza/kobart-summarization')
   model = BartForConditionalGeneration.from_pretrained('gogamza/kobart-summarization')
   

3. Prepare Input Text: Encode your input text using the tokenizer.

   text = "Your Korean text here"
   raw_input_ids = tokenizer.encode(text)
   input_ids = [tokenizer.bos_token_id] + raw_input_ids + [tokenizer.eos_token_id]
   

4. Generate Summary:

   summary_ids = model.generate(torch.tensor([input_ids]))
   summary = tokenizer.decode(summary_ids.squeeze().tolist(), skip_special_tokens=True)
   print(summary)
   

5. Cloud GPU Recommendation: For faster processing and handling larger datasets, consider using cloud GPU services such as AWS EC2, Google Cloud Platform, or Azure.

License

The KOBART-Summarization model is released under the MIT License, allowing for wide usage and modification with minimal restrictions.