Instructions to use tau/spider-nq-question-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tau/spider-nq-question-encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="tau/spider-nq-question-encoder")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("tau/spider-nq-question-encoder") model = AutoModel.from_pretrained("tau/spider-nq-question-encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tau/spider-nq-question-encoder: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/tau/spider-nq-question-encoder/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tau/spider-nq-question-encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tau/spider-nq-question-encoder/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 14516aa7c9190eb2dc53bd1d97e4432b74d9c6fc073a53bf3769b2ad0cd1ffcb
- Size of remote file:
- 438 MB
- SHA256:
- 2f5e53abaa91875fc4674df12e9ed13956973d7eb2204f0594e95df993d743e8
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