README.md

---
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