Instructions to use Yanhao/simcse-roberta-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Yanhao/simcse-roberta-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Yanhao/simcse-roberta-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Yanhao/simcse-roberta-large") model = AutoModel.from_pretrained("Yanhao/simcse-roberta-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Yanhao/simcse-roberta-large: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/Yanhao/simcse-roberta-large/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Yanhao/simcse-roberta-large/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Yanhao/simcse-roberta-large/resolve/main/pytorch_model.bin
1.42 GB
- Xet hash:
- 98c7c77d0e6bc1a045622697e4b6bca8616e586d9c8f55b246aef584df856fd0
- Size of remote file:
- 1.42 GB
- SHA256:
- b6bd83e8c46ae2d4c0ee035a8ce0d16c73cc4dd0369953db0e7b4cc2b61e9d94
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.