Instructions to use UdS-LSV/muv2x-simcse-smole-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UdS-LSV/muv2x-simcse-smole-bert with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, BertForCL tokenizer = AutoTokenizer.from_pretrained("UdS-LSV/muv2x-simcse-smole-bert") model = BertForCL.from_pretrained("UdS-LSV/muv2x-simcse-smole-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from UdS-LSV/muv2x-simcse-smole-bert: direct link, hf CLI and curl.
- Browser
- Download file 86.7 MB
-
https://huggingface.co/UdS-LSV/muv2x-simcse-smole-bert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://UdS-LSV/muv2x-simcse-smole-bert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/UdS-LSV/muv2x-simcse-smole-bert/resolve/main/pytorch_model.bin
86.7 MB
- Xet hash:
- 91a5a75489334d4b0a006155335421160db2e3091c981ae4276991dda4bb6088
- Size of remote file:
- 86.7 MB
- SHA256:
- dc15de7f06926f99ac359f4d61cffd0bec5e12cc101eff50ba81a126e3efe14d
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