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")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("BM-K/KoSimCSE-bert") model = AutoModel.from_pretrained("BM-K/KoSimCSE-bert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7b452f852e8d353a56e32a7cb2787601abbf08089227951d5c29b4f3743ceb1c
- Size of remote file:
- 443 MB
- SHA256:
- 47233538bdeab732414167f64cf74e55901f1d4b4008d563da0cedfe5a8a8364
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.