Create README.md

README.md

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+---
+language:
+- ko
+---
+# NewsKoT5
+The training data for this T5 model consists of Korean news articles (29GB). However, the performance has not been fine-tuned through the use of small batches and a limited number of training steps, so it may not be fully optimized.
+
+## Quick tour
+```python
+from transformers import AutoTokenizer, T5ForConditionalGeneration
+  
+tokenizer = AutoTokenizer.from_pretrained("BM-K/NewsKoT5-small")
+model = T5ForConditionalGeneration.from_pretrained("BM-K/NewsKoT5-small")
+
+input_ids = tokenizer("한국형발사체 누리호가 실용급 <extra_id_0> 발사체로서 ‘데뷔’를 성공적으로 <extra_id_1>", return_tensors="pt").input_ids
+labels = tokenizer("<extra_id_0> 위성 <extra_id_1> 마쳤다 <extra_id_2>", return_tensors="pt").input_ids
+
+outputs = model(input_ids=input_ids,
+                labels=labels)
+```
+
+## News Summarization Performance (F1-score)
+After restoring the model's tokenized output to the original text, Rouge performance was evaluated by comparing it to the reference and hypothesis tokenized using [mecab](https://konlpy.org/ko/v0.4.0/).
+
+- Dacon 한국어 문서 생성요약 AI 경진대회 [Dataset](https://dacon.io/competitions/official/235673/overview/description)
+    - Training: 29,432
+    - Validation: 7,358
+    - Test: 9,182
+
+| | #Param | rouge-1 |rouge-2|rouge-l|
+|-------|--------:|--------:|--------:|--------:|
+| pko-t5-small | 95M | 51.48 | 33.18 | 44.96 |
+| NewsT5-small | 61M | 52.15 | 33.59 | 45.41 |
+
+- AI-Hub 문서요약 텍스트 [Dataset](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=97)
+    - Training: 245,626
+    - Validation: 20,296
+    - Test: 9,931
+
+| | #Param | rouge-1 |rouge-2|rouge-l|
+|-------|--------:|--------:|--------:|--------:|
+| pko-t5-small | 95M | 53.44 | 34.03 | 45.36 |
+| NewsT5-small | 61M | 53.74 | 34.27 | 45.52 |
+
+- [pko-t5-small](https://github.com/paust-team/pko-t5)