Instructions to use castorini/bpr-nq-question-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use castorini/bpr-nq-question-encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="castorini/bpr-nq-question-encoder")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("castorini/bpr-nq-question-encoder") model = AutoModel.from_pretrained("castorini/bpr-nq-question-encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from castorini/bpr-nq-question-encoder: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/castorini/bpr-nq-question-encoder/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://castorini/bpr-nq-question-encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/castorini/bpr-nq-question-encoder/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 7f279a3aa47dd1c7e66f23f8659b3a53bbe9e49b1ed55eb641e10cb24c2b483c
- Size of remote file:
- 438 MB
- SHA256:
- 3cad265931a66f92edb33e66a2e2fab36322aade5f25461ddcaf2a55b7492788
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