Instructions to use bongsoo/kpf-cross-encoder-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bongsoo/kpf-cross-encoder-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bongsoo/kpf-cross-encoder-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bongsoo/kpf-cross-encoder-v1") model = AutoModelForSequenceClassification.from_pretrained("bongsoo/kpf-cross-encoder-v1") - Notebooks
- Google Colab
- Kaggle
Update model metadata to set pipeline tag to the new `text-ranking` and library name to `sentence-transformers`
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by tomaarsen HF Staff - opened
README.md
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license: apache-2.0
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language:
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- ko
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---
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# kpf-cross-encoder-v1
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- jinmang2/kpfbert 모델을 훈련시켜 cross-encoder로 파인튜닝한 모델
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license: apache-2.0
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language:
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- ko
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pipeline_tag: text-ranking
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library_name: sentence-transformers
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---
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# kpf-cross-encoder-v1
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- jinmang2/kpfbert 모델을 훈련시켜 cross-encoder로 파인튜닝한 모델
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