Instructions to use BM-K/KoSimCSE-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BM-K/KoSimCSE-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="BM-K/KoSimCSE-bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("BM-K/KoSimCSE-bert") model = AutoModel.from_pretrained("BM-K/KoSimCSE-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -16,7 +16,8 @@ def cal_score(a, b):
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b_norm = b / b.norm(dim=1)[:, None]
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return torch.mm(a_norm, b_norm.transpose(0, 1)) * 100
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model = AutoModel.from_pretrained('BM-K/KoSimCSE-bert')
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sentences = ['์นํ๊ฐ ๋คํ์ ๊ฐ๋ก ์ง๋ฌ ๋จน์ด๋ฅผ ์ซ๋๋ค.',
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'์นํ ํ ๋ง๋ฆฌ๊ฐ ๋จน์ด ๋ค์์ ๋ฌ๋ฆฌ๊ณ ์๋ค.',
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b_norm = b / b.norm(dim=1)[:, None]
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return torch.mm(a_norm, b_norm.transpose(0, 1)) * 100
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model = AutoModel.from_pretrained('BM-K/KoSimCSE-bert')
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AutoTokenizer.from_pretrained('BM-K/KoSimCSE-bert')
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sentences = ['์นํ๊ฐ ๋คํ์ ๊ฐ๋ก ์ง๋ฌ ๋จน์ด๋ฅผ ์ซ๋๋ค.',
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'์นํ ํ ๋ง๋ฆฌ๊ฐ ๋จน์ด ๋ค์์ ๋ฌ๋ฆฌ๊ณ ์๋ค.',
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