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--- |
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base_model: |
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- google-bert/bert-base-uncased |
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datasets: |
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- microsoft/ms_marco |
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language: |
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- en |
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library_name: transformers |
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license: apache-2.0 |
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pipeline_tag: feature-extraction |
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--- |
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# Model Card |
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This is the official model from the paper [Hypencoder: Hypernetworks for Information Retrieval](https://arxiv.org/abs/2502.05364). |
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## Model Details |
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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](https://github.com/jfkback/hypencoder-paper) page which contains the required code and details on how to run the model. |
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### Model Variants |
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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. |
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| Huggingface Repo | Number of Layers | |
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|:------------------:|:------------------:| |
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| [jfkback/hypencoder.2_layer](https://huggingface.co/jfkback/hypencoder.2_layer) | 2 | |
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| [jfkback/hypencoder.4_layer](https://huggingface.co/jfkback/hypencoder.4_layer) | 4 | |
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| [jfkback/hypencoder.6_layer](https://huggingface.co/jfkback/hypencoder.6_layer) | 6 | |
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| [jfkback/hypencoder.8_layer](https://huggingface.co/jfkback/hypencoder.8_layer) | 8 | |
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## Citation |
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**BibTeX:** |
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``` |
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@misc{killingback2025hypencoderhypernetworksinformationretrieval, |
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title={Hypencoder: Hypernetworks for Information Retrieval}, |
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author={Julian Killingback and Hansi Zeng and Hamed Zamani}, |
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year={2025}, |
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eprint={2502.05364}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.IR}, |
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url={https://arxiv.org/abs/2502.05364}, |
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} |
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``` |
