readme: 
  ---
  language: ko
  tags:
  - summarization
  - news
  inference: false
  ---
  
  <!-- 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. -->
  
  # KoT5_news_summarization
  
  - This model is a [lcw99/t5-base-korean-text-summary](https://huggingface.co/lcw99/t5-base-korean-text-summary) finetuned on the [daekeun-ml/naver-news-summarization-ko](https://huggingface.co/datasets/daekeun-ml/naver-news-summarization-ko)
  
  ## Model description
  
  <<20221021  Commit>>
  
  프로젝트용으로 뉴스 요약 모델 특화된 모델을 만들기 위해 lcw99님의 t5-base-korean-text-summary 모델에 추가적으로 daekeun-ml님이 제공해주신 naver-news-summarization-ko 데이터셋으로 파인튜닝 했습니다.
  
  현재 제가 가지고 있는 뉴스 데이터로 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> 
  
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 2e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - 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 |
  |:-------------:|:-----:|:-----:|:---------------:|
  | 0.4513        | 1.0   | 2775  | 0.4067          |
  | 0.42          | 2.0   | 5550  | 0.3933          |
  | 0.395         | 3.0   | 8325  | 0.3864          |
  | 0.3771        | 4.0   | 11100 | 0.3872          |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  