Instructions to use ffgcc/esimcse-roberta-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ffgcc/esimcse-roberta-large with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, RobertaForCL tokenizer = AutoTokenizer.from_pretrained("ffgcc/esimcse-roberta-large") model = RobertaForCL.from_pretrained("ffgcc/esimcse-roberta-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ffgcc/esimcse-roberta-large: direct link, hf CLI and curl.
- Browser
- Download file 2.85 GB
-
https://huggingface.co/ffgcc/esimcse-roberta-large/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ffgcc/esimcse-roberta-large/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ffgcc/esimcse-roberta-large/resolve/main/pytorch_model.bin
2.85 GB
- Xet hash:
- 0d061ae50b7b2769ec0ba53a5b9d40083b0b215187edb17ebf70c03f91b3e5dc
- Size of remote file:
- 2.85 GB
- SHA256:
- e8f0e898f4b03033687f3332ca6b9a51714f694a1ed87d169f9f64e430666d4b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.