Update README.md

README.md

@@ -1,9 +1,11 @@
+
 ---
+language: ko
 tags:
-- generated_from_trainer
-model-index:
-- name: KoT5_news_summarization
-  results: []
+- summarization
+- T5
+- news
+inference: false
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -11,23 +13,28 @@ should probably proofread and complete it, then remove this comment. -->
 
 # KoT5_news_summarization
 
-This model is a fine-tuned version of [lcw99/t5-base-korean-text-summary](https://huggingface.co/lcw99/t5-base-korean-text-summary) on an unknown dataset.
-It achieves the following results on the evaluation set:
+- 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)
+
 - Loss: 0.3872
 
 ## Model description
 
-More information needed
-
-## Intended uses & limitations
+<<20221021  Commit>>
 
-More information needed
+개인 스터디용으로 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.
 
-## Training and evaluation data
+<pre><code>
+# Python Code
+from transformers import AutoTokenizer
+from transformers import AutoModelForSeq2SeqLM
 
-More information needed
+tokenizer = AutoTokenizer.from_pretrained("noahkim/KoT5_news_summarization")
+model = AutoModelForSeq2SeqLM.from_pretrained("noahkim/KoT5_news_summarization")
+</pre></code> 
 
-## Training procedure
 
 ### Training hyperparameters