created model card

README.md

@@ -0,0 +1,42 @@
+---
+language:
+- en
+base_model:
+- google-t5/t5-large
+pipeline_tag: text-classification
+tags:
+- gen-ir
+- information-retrieval
+- ir
+---
+This repository contains one of the models analyzed in our paper [Reverse-Engineering the Retrieval Process in GenIR Models](https://dl.acm.org/doi/abs/10.1145/3726302.3730076).
+
+### Training
+The model is based on T5-large and was trained on the TriviaQA dataset (with only generated questions as input) as a atomic GenIR model using the setup of [DSI](https://arxiv.org/abs/2202.06991). 
+
+### Model Overview
+| Model        | Huggingface URL                                                         |
+| ------------ | ----------------------------------------------------------------------- |
+| NQ10k        | [DSI-large-NQ10k](https://huggingface.co/AnReu/DSI-large-NQ10k)         |
+| NQ100k       | [DSI-large-NQ100k](https://huggingface.co/AnReu/DSI-large-NQ100k)       |
+| NQ320k       | [DSI-large-NQ320k](https://huggingface.co/AnReu/DSI-large-NQ320k)       |
+| Trivia-QA    | [DSI-large-TriviaQA](https://huggingface.co/AnReu/DSI-large-TriviaQA)   |
+| Trivia-QA QG | [DSI-large-TriviaQA QG](https://huggingface.co/AnReu/DSI-large-TriviaQA-QG) |
+### Citation
+```
+@inproceedings{Reusch2025Reverse,
+  author = {Reusch, Anja and Belinkov, Yonatan},
+  title = {Reverse-Engineering the Retrieval Process in GenIR Models},
+  year = {2025},
+  isbn = {9798400715921},
+  publisher = {Association for Computing Machinery},
+  address = {New York, NY, USA},
+  url = {https://doi.org/10.1145/3726302.3730076},
+  doi = {10.1145/3726302.3730076},
+  booktitle = {Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval},
+  pages = {668–677},
+  numpages = {10},
+  location = {Padua, Italy},
+  series = {SIGIR '25}
+}
+```