README.md

---
library_name: transformers
license: apache-2.0
datasets:
- aimagelab/ReT-M2KR
base_model:
- laion/CLIP-ViT-H-14-laion2B-s32B-b79K
pipeline_tag: visual-document-retrieval
---

# Model Card: ReT-2

Official implementation of ReT-2: Recurrence Meets Transformers for Universal Multimodal Retrieval.

This model features visual and textual backbones based on [laion/CLIP-ViT-H-14-laion2B-s32B-b79K](https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K). 
<br>The backbones have been fine-tuned on the M2KR dataset. 


### Model Sources

<!-- Provide the basic links for the model. -->

- **Repository:** https://github.com/aimagelab/ReT-2
- **Paper:** [Recurrence Meets Transformers for Universal Multimodal Retrieval](https://arxiv.org/abs/2509.08897)


### Training Data
[aimagelab/ReT-M2KR](https://huggingface.co/datasets/aimagelab/ReT-M2KR)


## Citation
```
@article{caffagni2025recurrencemeetstransformers,
      title={{Recurrence Meets Transformers for Universal Multimodal Retrieval}}, 
      author={Davide Caffagni and Sara Sarto and Marcella Cornia and Lorenzo Baraldi and Rita Cucchiara},
      journal={arXiv preprint arXiv:2509.08897},
      year={2025}
}
```