KoT5_news_summarization
- This model is a lcw99/t5-base-korean-text-summary finetuned on the daekeun-ml/naver-news-summarization-ko
Model description
<<20221021 Commit>>
ํ๋ก์ ํธ์ฉ์ผ๋ก ๋ด์ค ์์ฝ ๋ชจ๋ธ ํนํ๋ ๋ชจ๋ธ์ ๋ง๋ค๊ธฐ ์ํด lcw99๋์ t5-base-korean-text-summary ๋ชจ๋ธ์ ์ถ๊ฐ์ ์ผ๋ก daekeun-ml๋์ด ์ ๊ณตํด์ฃผ์ naver-news-summarization-ko ๋ฐ์ดํฐ์ ์ผ๋ก ํ์ธํ๋ ํ์ต๋๋ค.
ํ์ฌ ์ ๊ฐ ๊ฐ์ง๊ณ ์๋ ๋ด์ค ๋ฐ์ดํฐ๋ก ์ถ๊ฐ ํ์ต ์งํ ์์ ์ ๋๋ค. ์ง์์ ์ผ๋ก ๋ฐ์ ์์ผ ์ข์ ์ฑ๋ฅ์ ๋ชจ๋ธ์ ๊ตฌํํ๊ฒ ์ต๋๋ค. ๊ฐ์ฌํฉ๋๋ค.
์คํํ๊ฒฝ
- Google Colab Pro
- CPU : Intel(R) Xeon(R) CPU @ 2.20GHz
- GPU : A100-SXM4-40GB
# 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")
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
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