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
- Xet hash:
- a4be9d7d11da1ef771da43fb430f911d0f54daf50325c4216642340305d802aa
- Size of remote file:
- 2.24 GB
- SHA256:
- 4eccdf260b9f0b9b5fc4e814e58a11ac4c9658c2aa8e8d0992a574110668adee
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.