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")# pip install -U transformers accelerate # 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
Commit History
-
Upload vocab.txt
Upload vocab.txtbf15a87 -
Upload tokenizer_config.json
Upload tokenizer_config.json43fac7b -
Upload tokenizer.json
Upload tokenizer.json0b637a1 -
Upload pytorch_model.bin with git-lfs
Upload pytorch_model.bin09913c1 -
Upload config.json
Upload config.json29164ad -
Create README.md
9ce6872 -
initial commit
d2ea761