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
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Download README.md from castorini/bpr-nq-question-encoder: direct link, hf CLI and curl.
- Browser
- Download file 270 Bytes
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https://huggingface.co/castorini/bpr-nq-question-encoder/resolve/main/README.md
- Command line
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hf download hf://castorini/bpr-nq-question-encoder/README.md
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curl -L -o README.md https://huggingface.co/castorini/bpr-nq-question-encoder/resolve/main/README.md
270 Bytes
This model is converted from the original BPR repo and fitted into Pyserini:
Ikuya Yamada, Akari Asai, and Hannaneh Hajishirzi. 2021. Efficient passage retrieval with hashing for open-domain question answering. arXiv:2106.00882.