Instructions to use Soyoung97/RankT5-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Soyoung97/RankT5-large with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Soyoung97/RankT5-large") model = AutoModelForSeq2SeqLM.from_pretrained("Soyoung97/RankT5-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Soyoung97/RankT5-large: direct link, hf CLI and curl.
- Browser
- Download file 2.95 GB
-
https://huggingface.co/Soyoung97/RankT5-large/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Soyoung97/RankT5-large/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Soyoung97/RankT5-large/resolve/main/pytorch_model.bin
2.95 GB
- Xet hash:
- 0b9ec40c8160d861365fb654a4ecf2ae5519c7f334d8aa9ad279698563a7592a
- Size of remote file:
- 2.95 GB
- SHA256:
- dd5a5ce2f23b1d4d388f89a0a220d19a86be22db25efd9be0b4358a1c12a690a
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