This is the official model from the paper Hypencoder: Hypernetworks for Information Retrieval.

This is a Hypencoder Dual Enocder. It contains two trunks the text encoder and Hypencoder. The text encoder converts items into 768 dimension vectors while the Hypencoder converts text into a small neural network which takes the 768 dimension vector from the text encoder as input. This small network is then used to output a relevance score. To use this model please take a look at the Github page which contains the required code and details on how to run the model.

We released the four models used in the paper. Each model is identical except the small neural networks, which we refer to as q-nets, have different numbers of hidden layers.

@misc{killingback2025hypencoderhypernetworksinformationretrieval,
      title={Hypencoder: Hypernetworks for Information Retrieval}, 
      author={Julian Killingback and Hansi Zeng and Hamed Zamani},
      year={2025},
      eprint={2502.05364},
      archivePrefix={arXiv},
      primaryClass={cs.IR},
      url={https://arxiv.org/abs/2502.05364}, 
}