Instructions to use castorini/mdpr-passage-nq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use castorini/mdpr-passage-nq with Transformers:
# Load model directly from transformers import AutoTokenizer, DPRContextEncoder tokenizer = AutoTokenizer.from_pretrained("castorini/mdpr-passage-nq") model = DPRContextEncoder.from_pretrained("castorini/mdpr-passage-nq", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 709b4bc32202a9bcce532c68e7169d5331d7e65a2e77855655a421a1279b1caf
- Size of remote file:
- 711 MB
- SHA256:
- 18ef025b1022e7309b7ec9ee6e05a11fd69b1b1dc6ee0f5fa0b946be701f7ece
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.