Text Classification
Transformers
PyTorch
Safetensors
Korean
English
xlm-roberta
text-embeddings-inference
Instructions to use Dongjin-kr/ko-reranker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dongjin-kr/ko-reranker with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Dongjin-kr/ko-reranker")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Dongjin-kr/ko-reranker") model = AutoModelForSequenceClassification.from_pretrained("Dongjin-kr/ko-reranker", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Update model metadata to set pipeline tag to the new `text-ranking` and library name to `sentence-transformers`
#2
by tomaarsen HF Staff - opened
README.md
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@@ -3,7 +3,8 @@ license: mit
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language:
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- ko
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- en
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pipeline_tag: text-
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---
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# Korean Reranker Training on Amazon SageMaker
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language:
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- ko
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- en
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pipeline_tag: text-ranking
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library_name: sentence-transformers
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---
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# Korean Reranker Training on Amazon SageMaker
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