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 training_args.bin from Yanhao/simcse-roberta-large: direct link, hf CLI and curl.
- Browser
- Download file 2.1 kB
-
https://huggingface.co/Yanhao/simcse-roberta-large/resolve/main/training_args.bin
- Command line
-
hf download hf://Yanhao/simcse-roberta-large/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Yanhao/simcse-roberta-large/resolve/main/training_args.bin
2.1 kB
- Xet hash:
- fec881a239fa65c7d0938193a2d3b89bf6a590f913a04c724f7b28367237d758
- Size of remote file:
- 2.1 kB
- SHA256:
- cf1026702ae4cfdfbc5fdc96f74f0a29b5075b9fddbfe0061b9d0fc9d0ed38dd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.