Instructions to use BM-K/KoSimCSE-roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BM-K/KoSimCSE-roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="BM-K/KoSimCSE-roberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("BM-K/KoSimCSE-roberta") model = AutoModel.from_pretrained("BM-K/KoSimCSE-roberta", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from BM-K/KoSimCSE-roberta: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/BM-K/KoSimCSE-roberta/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://BM-K/KoSimCSE-roberta/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/BM-K/KoSimCSE-roberta/resolve/main/pytorch_model.bin
443 MB
- Xet hash:
- 5367774b8300f4bee18202753074c5dc9edbe46d85bac2795c953cb2d9011369
- Size of remote file:
- 443 MB
- SHA256:
- 9941030acf7c0e0da0560d26ca00b97b701d4d737946611165e4d106cdf5c476
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.