readme: ---
  language: ko
  tags:
  - summarization
  - news
  inference: false
  model-index:
  - name: KoBigBird-KoBart-News-Summarization
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # KoBigBird-KoBart-News-Summarization
  
  This model is a fine-tuned version of [noahkim/KoBigBird-KoBart-News-Summarization](https://huggingface.co/noahkim/KoBigBird-KoBart-News-Summarization) on the [daekeun-ml/naver-news-summarization-ko](https://huggingface.co/datasets/daekeun-ml/naver-news-summarization-ko)
  
  
  ## Model description
  
  <<20221110  Commit>>
  
  <<KoBigBird-KoBart-News-Summarization 모델 설명>>
  
  다중문서요약(Multi-Document-Summarization) Task를 위해서 KoBigBird 모델을 Encoder-Decoder모델을 만들어서 학습을 진행했습니다. KoBigBird를 Decoder로 쓰려고 했으나 오류가 생겨서 요약에 특화된 KoBART의 Decoder를 활용해서 모델을 생성했습니다.
  
  프로젝트용으로 뉴스 요약 모델 특화된 모델을 만들기 위해 기존에 만들었던 KoBigBird-KoBart-News-Summarization 모델에 추가적으로 daekeun-ml님이 제공해주신 naver-news-summarization-ko 데이터셋으로 파인튜닝 했습니다.
  
  현재 AI-HUB에서 제공하는 요약 데이터를 추가 학습 진행 예정입니다.
  지속적으로 발전시켜 좋은 성능의 모델을 구현하겠습니다.
  감사합니다.
  
  실행환경
  - Google Colab Pro
  - CPU : Intel(R) Xeon(R) CPU @ 2.20GHz
  - GPU : A100-SXM4-40GB
  
  <pre><code>
  # Python Code
  from transformers import AutoTokenizer
  from transformers import AutoModelForSeq2SeqLM
  
  tokenizer = AutoTokenizer.from_pretrained("noahkim/KoT5_news_summarization")
  model = AutoModelForSeq2SeqLM.from_pretrained("noahkim/KoT5_news_summarization")
  </pre></code> 
  
  
  The following hyperparameters were used during training:
  - learning_rate: 2e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 4
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss |
  |:-------------:|:-----:|:----:|:---------------:|
  | 4.0748        | 1.0   | 1388 | 4.3067          |
  | 3.8457        | 2.0   | 2776 | 4.2039          |
  | 3.7459        | 3.0   | 4164 | 4.1433          |
  | 3.6773        | 4.0   | 5552 | 4.1236          |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.1
  - Tokenizers 0.13.2
  