Instructions to use castorini/bpr-nq-ctx-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use castorini/bpr-nq-ctx-encoder with Transformers:
# Load model directly from transformers import AutoTokenizer, DPRContextEncoder tokenizer = AutoTokenizer.from_pretrained("castorini/bpr-nq-ctx-encoder") model = DPRContextEncoder.from_pretrained("castorini/bpr-nq-ctx-encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from castorini/bpr-nq-ctx-encoder: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/castorini/bpr-nq-ctx-encoder/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://castorini/bpr-nq-ctx-encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/castorini/bpr-nq-ctx-encoder/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- c2c6619bc205cd6c7c3f98f0d1ccf4945e8528a91cdc7f4605b05ae9607bf85f
- Size of remote file:
- 438 MB
- SHA256:
- ff71c8828ff312ea197499d17332e30212983822badbd636fb1c00f2e8dca2ba
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