Instructions to use kompactss/JeBERT_je_ko with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kompactss/JeBERT_je_ko with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("kompactss/JeBERT_je_ko") model = AutoModelForSeq2SeqLM.from_pretrained("kompactss/JeBERT_je_ko", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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# ๐ ์ ์ฃผ ๋ฐฉ์ธ ๋ฒ์ญ ๋ชจ๋ธ ๐
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## 1. Seq2Seq Transformer Model
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## 2. Dataset
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## 3. Hyper Parameters
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## 4. BLEU Score
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# ๐ ์ ์ฃผ ๋ฐฉ์ธ ๋ฒ์ญ ๋ชจ๋ธ ๐
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- ์ ์ฃผ์ด -> ํ์ค์ด
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- Made by. ๊ตฌ๋ฆ ์์ฐ์ด์ฒ๋ฆฌ ๊ณผ์ 3๊ธฐ 3์กฐ!!
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## 1. Seq2Seq Transformer Model
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- encoder : Bert
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- decoder : Bert
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- Tokenizer : WordPiece Tokenizer
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## 2. Dataset
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- Jit Dataset
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- AI HUB(+์๋์ ๋ฌธ์)
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## 3. Hyper Parameters
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- Epoch : 10 epochs(best at 8 epoch)
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- Random Seed : 42
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- Learning Rate : 5e-5
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- Warm up Ratio : 0.1
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- Batch Size : 32
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## 4. BLEU Score
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- Jit + AI HUB(+์๋์ ๋ฌธ์) Dataset : 79.0
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