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
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
.
More Information About NewsKoT5-small huggingface.co Model
NewsKoT5-small huggingface.co
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BM-K NewsKoT5-small online free url in huggingface.co:
NewsKoT5-small is an open source model from GitHub that offers a free installation service, and any user can find NewsKoT5-small on GitHub to install. At the same time, huggingface.co provides the effect of NewsKoT5-small install, users can directly use NewsKoT5-small installed effect in huggingface.co for debugging and trial. It also supports api for free installation.